Trust in Artificial Intelligent Agents Scale

First quantitative approbation. Data analysis workflow

HSE University

---

Preprocess

Find preprocess workflow here.

Packages

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.4     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.0

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(psych)

## 
## Attaching package: 'psych'

## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha

library(lavaan)

## This is lavaan 0.6-8
## lavaan is FREE software! Please report any bugs.

## 
## Attaching package: 'lavaan'

## The following object is masked from 'package:psych':
## 
##     cor2cov

library(semPlot)
library(knitr)
library(ggcorrplot)

theme_set(theme_bw())

Import data

taia <- read_csv("https://github.com/angelgardt/taia/raw/master/data/taia.csv")

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   .default = col_double(),
##   id = col_character(),
##   oth_text = col_character(),
##   expoth_text = col_character(),
##   sex = col_character(),
##   edulvl1 = col_character(),
##   spec1 = col_character(),
##   edulvl2 = col_character(),
##   spec2 = col_character(),
##   jobfield = col_character(),
##   jobpos = col_character(),
##   city = col_character()
## )
## ℹ Use `spec()` for the full column specifications.

str(taia)

## tibble [495 × 133] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ id         : chr [1:495] "00XzIUUVmQ" "0aABrq9MBY" "0c6myGTrKr" "0CS5iaAVos" ...
##  $ e_dighelp  : num [1:495] 5 NA 4 3.33 3 ...
##  $ n_dighelp  : num [1:495] 1 NA 1 3 2 1 3 2 4 1 ...
##  $ e_socnet   : num [1:495] 0 NA 3.2 3.6 4.5 ...
##  $ f_socnet   : num [1:495] 3 NA 2.4 1.6 2 ...
##  $ n_socnet   : num [1:495] 2 NA 5 5 2 2 2 4 3 4 ...
##  $ gt_score   : num [1:495] 2.67 2.67 2.83 2.5 2.67 ...
##  $ pr01       : num [1:495] 3 3 3 3 3 4 3 1 3 4 ...
##  $ pr02       : num [1:495] 3 3 3 3 3 3 3 1 3 3 ...
##  $ pr03       : num [1:495] 3 3 3 3 3 1 5 1 3 4 ...
##  $ pr04       : num [1:495] 2 3 3 3 2 0 3 2 4 3 ...
##  $ pr05       : num [1:495] 3 2 1 2 3 4 4 1 0 3 ...
##  $ pr06       : num [1:495] 2 2 4 3 3 5 4 3 4 4 ...
##  $ pr07       : num [1:495] 3 3 3 3 3 5 3 2 1 3 ...
##  $ pr08       : num [1:495] 2 3 4 2 3 4 4 4 3 4 ...
##  $ pr09       : num [1:495] 2 4 3 3 4 4 4 3 3 4 ...
##  $ pr10       : num [1:495] 2 3 3 2 3 4 3 3 1 3 ...
##  $ co01       : num [1:495] 2 3 3 2 3 4 3 2 4 3 ...
##  $ co02       : num [1:495] 3 3 3 2 3 3 3 1 2 3 ...
##  $ co03       : num [1:495] 3 4 4 2 3 4 4 2 2 3 ...
##  $ co04       : num [1:495] 4 2 4 4 3 5 4 3 5 4 ...
##  $ co05       : num [1:495] 2 2 3 2 3 4 4 2 3 2 ...
##  $ co06       : num [1:495] 3 3 4 4 3 3 4 2 2 3 ...
##  $ co07       : num [1:495] 2 3 1 1 1 1 1 3 0 1 ...
##  $ co08       : num [1:495] 2 2 2 1 4 5 2 1 2 1 ...
##  $ co09       : num [1:495] 2 3 2 1 4 4 3 2 1 2 ...
##  $ co10       : num [1:495] 3 2 3 2 3 5 3 1 1 2 ...
##  $ ut01       : num [1:495] 3 4 4 5 4 5 5 4 4 3 ...
##  $ ut02       : num [1:495] 2 3 3 4 4 5 5 3 3 3 ...
##  $ ut03       : num [1:495] 3 2 4 5 1 5 3 4 3 3 ...
##  $ ut04       : num [1:495] 3 3 3 4 4 5 4 3 2 3 ...
##  $ ut05       : num [1:495] 3 2 3 5 4 3 4 3 4 3 ...
##  $ ut06       : num [1:495] 2 3 4 4 4 5 4 3 3 3 ...
##  $ ut07       : num [1:495] 3 2 4 5 4 4 4 3 4 3 ...
##  $ ut08       : num [1:495] 3 3 3 4 4 5 4 3 4 4 ...
##  $ ut09       : num [1:495] 2 3 3 3 3 4 4 3 3 4 ...
##  $ ut10       : num [1:495] 2 2 2 4 1 2 2 1 3 3 ...
##  $ ut11       : num [1:495] 2 2 3 4 3 2 4 1 1 3 ...
##  $ ut12       : num [1:495] 3 4 4 3 4 2 4 3 3 4 ...
##  $ fa01       : num [1:495] 2 2 3 4 2 2 3 3 2 2 ...
##  $ fa02       : num [1:495] 2 3 2 4 2 0 2 3 2 1 ...
##  $ fa03       : num [1:495] 3 3 3 1 4 2 1 0 3 2 ...
##  $ fa04       : num [1:495] 2 3 3 2 1 2 2 0 1 2 ...
##  $ fa05       : num [1:495] 3 3 3 3 4 4 3 3 2 3 ...
##  $ fa06       : num [1:495] 3 2 4 3 3 2 4 0 2 3 ...
##  $ fa07       : num [1:495] 3 3 3 3 1 3 2 1 1 3 ...
##  $ fa08       : num [1:495] 3 2 2 2 1 2 2 3 2 2 ...
##  $ fa09       : num [1:495] 3 2 3 4 2 1 2 3 2 2 ...
##  $ fa10       : num [1:495] 2 2 2 4 1 3 3 4 4 2 ...
##  $ de01       : num [1:495] 3 2 3 3 3 3 4 3 3 3 ...
##  $ de02       : num [1:495] 3 3 3 3 3 4 4 2 1 3 ...
##  $ de03       : num [1:495] 3 3 4 3 3 5 3 2 1 3 ...
##  $ de04       : num [1:495] 2 3 2 0 2 2 0 3 1 3 ...
##  $ de05       : num [1:495] 3 3 4 3 3 5 5 4 4 4 ...
##  $ de06       : num [1:495] 2 3 3 3 3 3 4 0 0 3 ...
##  $ de07       : num [1:495] 3 2 3 3 3 3 4 3 3 3 ...
##  $ de08       : num [1:495] 3 3 3 3 3 5 4 1 1 3 ...
##  $ de09       : num [1:495] 4 2 3 3 3 5 5 4 1 4 ...
##  $ de10       : num [1:495] 3 3 4 3 3 3 4 3 3 3 ...
##  $ de11       : num [1:495] 2 3 2 3 2 1 4 4 1 1 ...
##  $ un01       : num [1:495] 3 3 3 2 4 3 4 3 4 4 ...
##  $ un02       : num [1:495] 2 2 3 2 4 3 4 2 3 3 ...
##  $ un03       : num [1:495] 3 1 3 3 4 5 4 2 4 3 ...
##  $ un04       : num [1:495] 3 1 3 3 4 3 3 2 4 3 ...
##  $ un05       : num [1:495] 3 3 4 3 4 4 4 3 4 4 ...
##  $ un06       : num [1:495] 3 3 2 1 1 1 4 1 4 4 ...
##  $ un07       : num [1:495] 2 3 2 2 4 4 3 1 2 3 ...
##  $ un08       : num [1:495] 2 3 3 3 4 4 4 4 4 3 ...
##  $ un09       : num [1:495] 2 1 4 2 4 4 4 1 2 4 ...
##  $ un10       : num [1:495] 3 3 3 2 4 3 4 2 2 4 ...
##  $ un11       : num [1:495] 3 2 3 2 4 4 3 3 4 3 ...
##  $ un12       : num [1:495] 3 2 3 3 4 4 4 3 4 3 ...
##  $ gt01       : num [1:495] 3 3 3 2 3 2 1 1 1 2 ...
##  $ gt02       : num [1:495] 3 2 3 3 3 3 1 1 1 2 ...
##  $ gt03       : num [1:495] 3 3 3 3 3 2 1 1 1 3 ...
##  $ gt04       : num [1:495] 3 2 3 2 3 4 1 1 2 2 ...
##  $ gt05       : num [1:495] 2 3 2 2 1 4 0 2 0 2 ...
##  $ gt06       : num [1:495] 2 3 3 3 3 3 3 1 1 3 ...
##  $ socnet     : num [1:495] 1 0 1 1 1 1 1 1 1 1 ...
##  $ vk         : num [1:495] 1 -2 1 1 1 1 1 1 1 1 ...
##  $ fb         : num [1:495] 0 -2 1 1 1 0 0 1 0 1 ...
##  $ tw         : num [1:495] 0 -2 1 0 0 0 0 0 0 0 ...
##  $ in         : num [1:495] 1 -2 1 1 0 1 1 1 1 1 ...
##  $ tt         : num [1:495] 0 -2 0 1 0 0 0 0 0 0 ...
##  $ yt         : num [1:495] 0 -2 1 1 0 0 0 1 1 1 ...
##  $ freqvk     : num [1:495] 3 -2 3 2 3 3 3 3 3 3 ...
##  $ freqfb     : num [1:495] -2 -2 2 2 1 -2 -2 0 -2 3 ...
##  $ freqtw     : num [1:495] -2 -2 2 -2 -2 -2 -2 -2 -2 -2 ...
##  $ freqin     : num [1:495] 3 -2 3 1 -2 3 3 2 3 3 ...
##  $ freqtt     : num [1:495] -2 -2 -2 1 -2 -2 -2 -2 -2 -2 ...
##  $ freqyt     : num [1:495] -2 -2 2 2 -2 -2 -2 2 2 3 ...
##  $ expvk      : num [1:495] 0 -2 4 3 5 3 5 4 3 3 ...
##  $ expfb      : num [1:495] -2 -2 3 3 4 -2 -2 2 -2 2 ...
##  $ exptw      : num [1:495] -2 -2 2 -2 -2 -2 -2 -2 -2 -2 ...
##  $ expin      : num [1:495] 0 -2 4 4 -2 4 5 2 2 4 ...
##  $ exptt      : num [1:495] -2 -2 -2 4 -2 -2 -2 -2 -2 -2 ...
##  $ expyt      : num [1:495] -2 -2 3 4 -2 -2 -2 4 2 3 ...
##  $ dighelp    : num [1:495] 1 0 1 1 1 1 1 1 1 1 ...
##  $ siri       : num [1:495] 0 -2 0 1 0 0 1 0 1 0 ...
##   [list output truncated]
##  - attr(*, "spec")=
##   .. cols(
##   ..   id = col_character(),
##   ..   e_dighelp = col_double(),
##   ..   n_dighelp = col_double(),
##   ..   e_socnet = col_double(),
##   ..   f_socnet = col_double(),
##   ..   n_socnet = col_double(),
##   ..   gt_score = col_double(),
##   ..   pr01 = col_double(),
##   ..   pr02 = col_double(),
##   ..   pr03 = col_double(),
##   ..   pr04 = col_double(),
##   ..   pr05 = col_double(),
##   ..   pr06 = col_double(),
##   ..   pr07 = col_double(),
##   ..   pr08 = col_double(),
##   ..   pr09 = col_double(),
##   ..   pr10 = col_double(),
##   ..   co01 = col_double(),
##   ..   co02 = col_double(),
##   ..   co03 = col_double(),
##   ..   co04 = col_double(),
##   ..   co05 = col_double(),
##   ..   co06 = col_double(),
##   ..   co07 = col_double(),
##   ..   co08 = col_double(),
##   ..   co09 = col_double(),
##   ..   co10 = col_double(),
##   ..   ut01 = col_double(),
##   ..   ut02 = col_double(),
##   ..   ut03 = col_double(),
##   ..   ut04 = col_double(),
##   ..   ut05 = col_double(),
##   ..   ut06 = col_double(),
##   ..   ut07 = col_double(),
##   ..   ut08 = col_double(),
##   ..   ut09 = col_double(),
##   ..   ut10 = col_double(),
##   ..   ut11 = col_double(),
##   ..   ut12 = col_double(),
##   ..   fa01 = col_double(),
##   ..   fa02 = col_double(),
##   ..   fa03 = col_double(),
##   ..   fa04 = col_double(),
##   ..   fa05 = col_double(),
##   ..   fa06 = col_double(),
##   ..   fa07 = col_double(),
##   ..   fa08 = col_double(),
##   ..   fa09 = col_double(),
##   ..   fa10 = col_double(),
##   ..   de01 = col_double(),
##   ..   de02 = col_double(),
##   ..   de03 = col_double(),
##   ..   de04 = col_double(),
##   ..   de05 = col_double(),
##   ..   de06 = col_double(),
##   ..   de07 = col_double(),
##   ..   de08 = col_double(),
##   ..   de09 = col_double(),
##   ..   de10 = col_double(),
##   ..   de11 = col_double(),
##   ..   un01 = col_double(),
##   ..   un02 = col_double(),
##   ..   un03 = col_double(),
##   ..   un04 = col_double(),
##   ..   un05 = col_double(),
##   ..   un06 = col_double(),
##   ..   un07 = col_double(),
##   ..   un08 = col_double(),
##   ..   un09 = col_double(),
##   ..   un10 = col_double(),
##   ..   un11 = col_double(),
##   ..   un12 = col_double(),
##   ..   gt01 = col_double(),
##   ..   gt02 = col_double(),
##   ..   gt03 = col_double(),
##   ..   gt04 = col_double(),
##   ..   gt05 = col_double(),
##   ..   gt06 = col_double(),
##   ..   socnet = col_double(),
##   ..   vk = col_double(),
##   ..   fb = col_double(),
##   ..   tw = col_double(),
##   ..   `in` = col_double(),
##   ..   tt = col_double(),
##   ..   yt = col_double(),
##   ..   freqvk = col_double(),
##   ..   freqfb = col_double(),
##   ..   freqtw = col_double(),
##   ..   freqin = col_double(),
##   ..   freqtt = col_double(),
##   ..   freqyt = col_double(),
##   ..   expvk = col_double(),
##   ..   expfb = col_double(),
##   ..   exptw = col_double(),
##   ..   expin = col_double(),
##   ..   exptt = col_double(),
##   ..   expyt = col_double(),
##   ..   dighelp = col_double(),
##   ..   siri = col_double(),
##   ..   alice = col_double(),
##   ..   salut = col_double(),
##   ..   oleg = col_double(),
##   ..   alex = col_double(),
##   ..   mia = col_double(),
##   ..   mts = col_double(),
##   ..   ggle = col_double(),
##   ..   oth = col_double(),
##   ..   oth_text = col_character(),
##   ..   expsiri = col_double(),
##   ..   expalice = col_double(),
##   ..   expsalut = col_double(),
##   ..   expoleg = col_double(),
##   ..   expalex = col_double(),
##   ..   expmia = col_double(),
##   ..   expmts = col_double(),
##   ..   expggle = col_double(),
##   ..   expoth = col_double(),
##   ..   expoth_text = col_character(),
##   ..   selfdrcar = col_double(),
##   ..   selfdrexp = col_double(),
##   ..   selfdrsafe = col_double(),
##   ..   eduai = col_double(),
##   ..   eduaiexp = col_double(),
##   ..   age = col_double(),
##   ..   sex = col_character(),
##   ..   edulvl1 = col_character(),
##   ..   spec1 = col_character(),
##   ..   edu2 = col_double(),
##   ..   edulvl2 = col_character(),
##   ..   spec2 = col_character(),
##   ..   jobfield = col_character(),
##   ..   jobpos = col_character(),
##   ..   city = col_character()
##   .. )

Preparation

Vectors of TAIA items:

pr_items_0 <- colnames(taia)[8:17]
co_items_0 <- colnames(taia)[18:27]
ut_items_0 <- colnames(taia)[28:39]
fa_items_0 <- colnames(taia)[40:49]
de_items_0 <- colnames(taia)[50:60]
un_items_0 <- colnames(taia)[61:72]
taia_items_0 <- colnames(taia)[8:72]

Vector of GT items:

gt_items <- colnames(taia)[73:78]

Column names for further formatting:

col_names <- c("", "Num. of obs.", "Mean", "SD",
               "Median", "Trimmed Mean", "MAD",
               "Min", "Max", "Range",
               "Skewness", "Kurtuosis", "SE")
total_colnames <- c("Alpha", "Standardized Alpha", "Guttman's Lambda 6",
                    "Average interitem correlation", "S/N",
                    "Alpha SE", "Scale Mean", "Total Score SD",
                    "Median interitem correlation")
item_stats_colnames <- c("Num. of Obs.", "Discrimination",
                         "Std Cor",
                         "Cor Overlap Corrected",
                         "Cor if drop",
                         "Difficulty", "SD")
alpha_drop_colnames <- c("Alpha", "Standardized Alpha",
                "Guttman's Lambda 6",   "Average interitem correlation",
                "S/N",  "Alpha SE", "Var(r)","Median interitem correlation")

Exploratory analysis

TAIA descriptive statistics

Predictability

taia %>% 
  select(all_of(pr_items_0)) %>% 
  describe() %>% 
  kable(caption = "Predictability", label = 1, digits = 2, col.names = col_names)

Predictability

		Num. of obs.	Mean	SD	Median	Trimmed Mean	MAD	Min	Max	Range	Skewness	Kurtuosis	SE
pr01	1	495	2.84	0.99	3	2.87	1.48	0	5	5	-0.28	0.37	0.04
pr02	2	495	2.73	0.97	3	2.77	1.48	0	5	5	-0.19	0.10	0.04
pr03	3	495	2.89	1.01	3	2.91	1.48	0	5	5	-0.15	-0.05	0.05
pr04	4	495	2.84	1.04	3	2.87	1.48	0	5	5	-0.18	-0.04	0.05
pr05	5	495	2.22	1.20	2	2.22	1.48	0	5	5	0.03	-0.31	0.05
pr06	6	495	3.04	1.07	3	3.06	1.48	0	5	5	-0.26	0.07	0.05
pr07	7	495	2.59	1.11	3	2.61	1.48	0	5	5	-0.15	-0.10	0.05
pr08	8	495	3.05	0.91	3	3.09	0.00	0	5	5	-0.56	1.26	0.04
pr09	9	495	2.89	0.95	3	2.94	0.00	0	5	5	-0.50	1.05	0.04
pr10	10	495	2.83	1.04	3	2.90	1.48	0	5	5	-0.41	0.28	0.05

taia %>% select(all_of(pr_items_0)) %>% 
  pivot_longer(cols = all_of(pr_items_0)) %>% 
  ggplot(aes(value)) +
  geom_bar(fill = "darkred") +
  facet_wrap(~ name) +
  scale_x_discrete(limits = 0:5) +
  labs(x = "Score", y = "Number of observations",
       title = "Predictability") +
  theme(plot.title = element_text(hjust = .5))

## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?

• pr01 OK
  • Я считаю, что интеллектуальные системы надежны
• pr02 OK
  • Я считаю, что результаты работы интеллектуальных систем хорошо предсказуемы
• pr03 OK
  • Я считаю, что интеллектуальные системы ненадежны (R)
• pr04 OK
  • Я считаю, что результаты работы интеллектуальных систем невозможно предсказать (R)
• pr05 positive skewness
  • Я считаю, что поездки в машине, управляемой искусственным интеллектом, безопаснее, чем в обычной
• pr06 OK
  • Я считаю, что медицинская диагностика с применением интеллектуальных систем надеждее, чем без них
• pr07 OK
  • Я считаю, что финансовые операции под контролем искусственного интеллекта безопаснее, чем обычные
• pr08 high kurtosis
  • Я считаю, что интеллектуальные системы, обеспечивающие безопасность домов, хорошо справляются со своей задачей
• pr09 high kurtosis
  • Я считаю, что рекомендательные системы чаще всего правильно определяют предпочтения пользователей
• pr10 OK

Consistency

taia %>% 
  select(all_of(co_items_0)) %>% 
  describe() %>% 
  kable(caption = "Consistency", label = 2, digits = 2, col.names = col_names)

Consistency

		Num. of obs.	Mean	SD	Median	Trimmed Mean	MAD	Min	Max	Range	Skewness	Kurtuosis	SE
co01	1	495	2.49	1.08	3	2.51	1.48	0	5	5	-0.17	0.13	0.05
co02	2	495	2.51	1.04	3	2.53	1.48	0	5	5	-0.19	-0.06	0.05
co03	3	495	2.86	1.02	3	2.92	1.48	0	5	5	-0.40	0.31	0.05
co04	4	495	3.47	1.09	4	3.54	1.48	0	5	5	-0.57	0.32	0.05
co05	5	495	2.20	1.11	2	2.17	1.48	0	5	5	0.10	-0.17	0.05
co06	6	495	2.52	1.11	3	2.53	1.48	0	5	5	-0.13	-0.15	0.05
co07	7	495	1.59	1.13	2	1.52	1.48	0	5	5	0.54	0.12	0.05
co08	8	495	1.90	1.04	2	1.86	1.48	0	5	5	0.40	0.28	0.05
co09	9	495	2.05	1.07	2	2.01	1.48	0	5	5	0.35	0.12	0.05
co10	10	495	2.44	1.10	2	2.44	1.48	0	5	5	-0.04	-0.06	0.05

taia %>% select(all_of(co_items_0)) %>% 
  pivot_longer(cols = all_of(co_items_0)) %>% 
  ggplot(aes(value)) +
  geom_bar(fill = "chocolate3") +
  facet_wrap(~ name) +
  scale_x_discrete(limits = 0:5) +
  labs(x = "Score", y = "Number of observations",
       title = "Consistency") +
  theme(plot.title = element_text(hjust = .5))

## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?

• co01 OK
  • Интеллектуальные системы надежны, так как те системы, с которыми я сталкивался (сталкивалась), были надежными
• co02 OK
  • Я считаю, что если система при тестировании работает корректно, то и дальше она будет работать корректно
• co03 high kurtosis
  • Я считаю, что интеллектуальные системы совершают меньше технических ошибок, чем люди
• co04 high negative skewness
  • Со временем любая интеллектуальная система будет совершать всё меньше ошибок
• co05 positive skewness
  • Если интеллектуальные системы, с которыми я сталкивался (сталкивалась), были надежными, то я могу считать надёжными другие системы
• co06 positive skewness
  • Я могу судить о надёжности новой интеллектуальной системы по опыту работы с другими системами
• co07 high positive skewness
  • Я могу сказать, что система работает корректно, только протестировав её (R)
• co08 positive skewness
  • Если одна интеллектуальная система подводит меня, то и с другими будет то же самое
• co09positive skewness
  • Если одна интеллектуальная система соответствует моим ожиданиям, то с другими будет то же самое
• co10 positive skewness
  • Я могу сформировать ожидания относительно работы интеллектуальных систем в целом на основе опыта взаимодействия с одной системой

Utility

taia %>% 
  select(all_of(ut_items_0)) %>% 
  describe() %>% 
  kable(caption = "Utility", label = 3, digits = 2, col.names = col_names)

Utility

		Num. of obs.	Mean	SD	Median	Trimmed Mean	MAD	Min	Max	Range	Skewness	Kurtuosis	SE
ut01	1	495	3.78	1.05	4	3.88	1.48	0	5	5	-0.86	1.12	0.05
ut02	2	495	3.52	1.05	3	3.59	1.48	0	5	5	-0.53	0.50	0.05
ut03	3	495	3.56	1.11	4	3.64	1.48	0	5	5	-0.57	0.15	0.05
ut04	4	495	3.09	1.11	3	3.15	1.48	0	5	5	-0.43	0.03	0.05
ut05	5	495	3.05	1.21	3	3.09	1.48	0	5	5	-0.33	-0.15	0.05
ut06	6	495	3.27	1.10	3	3.31	1.48	0	5	5	-0.61	0.68	0.05
ut07	7	495	3.20	1.13	3	3.23	1.48	0	5	5	-0.28	-0.22	0.05
ut08	8	495	3.44	1.05	3	3.49	1.48	0	5	5	-0.55	0.44	0.05
ut09	9	495	3.18	1.17	3	3.23	1.48	0	5	5	-0.47	0.16	0.05
ut10	10	495	2.17	1.11	2	2.16	1.48	0	5	5	0.08	-0.23	0.05
ut11	11	495	2.67	1.23	3	2.69	1.48	0	5	5	-0.11	-0.41	0.06
ut12	12	495	3.16	1.15	3	3.21	1.48	0	5	5	-0.42	0.03	0.05

taia %>% select(all_of(ut_items_0)) %>% 
  pivot_longer(cols = all_of(ut_items_0)) %>% 
  ggplot(aes(value)) +
  geom_bar(fill = "goldenrod3") +
  facet_wrap(~ name) +
  scale_x_discrete(limits = 0:5) +
  labs(x = "Score", y = "Number of observations",
       title = "Utility") +
  theme(plot.title = element_text(hjust = .5))

## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?

• ut01 extremely high negative skewness
  • Я считаю, что интеллектуальные технологии — неотъемлемая часть развития общества
• ut02 high negative skewness
  • Я считаю, что человечество нуждается в интеллектуальных системах
• ut03 high negative skewness
  • Мне кажется, человечеству будет лучше без интеллектуальных систем (R)
• ut04 negative skewness
  • Я считаю, что развитие интеллектуальных систем — это оправданный риск для общества
• ut05 OK
  • По моему мнению, без интеллектуальных систем общественный прогресс остановился бы
• ut06 negative skewness
  • Я считаю, что интеллектуальные системы существенно повышают качество медицинской диагностики
• ut07 light negative skewness
  • Мне кажется, что оформить услуги с цифровыми помощниками гораздо проще, чем без них
• ut08 negative skewness
  • Мне кажется, рекомендательные сервисы существенно сокращают время поиска нужной информации
• ut09 negative skewness
  • Я считаю, что если полиция будет использовать интеллектуальные системы, то количество раскрытых преступлений увеличится
• ut10 positive skewness
  • Мне кажется, при оформлении услуг через интернет можно справиться и без цифровых помощников (R)
• ut11 OK
  • Я считаю, что если на дорогах появятся машины, управляемые искусственным интеллектом, то число аварий снизится
• ut12 negative skewness
  • Я считаю, что использование интеллектуальных систем в обучении повышает качество образования

Faith

taia %>% 
  select(all_of(fa_items_0)) %>% 
  describe() %>% 
  kable(caption = "Faith", label = 4, digits = 2, col.names = col_names)

Faith

		Num. of obs.	Mean	SD	Median	Trimmed Mean	MAD	Min	Max	Range	Skewness	Kurtuosis	SE
fa01	1	495	2.42	1.10	2	2.42	1.48	0	5	5	-0.02	-0.29	0.05
fa02	2	495	2.16	1.18	2	2.15	1.48	0	5	5	0.18	-0.42	0.05
fa03	3	495	1.51	1.13	1	1.42	1.48	0	5	5	0.66	0.16	0.05
fa04	4	495	1.57	1.08	1	1.51	1.48	0	5	5	0.55	0.17	0.05
fa05	5	495	2.46	1.10	2	2.48	1.48	0	5	5	-0.15	-0.10	0.05
fa06	6	495	2.47	1.08	3	2.49	1.48	0	5	5	-0.18	0.06	0.05
fa07	7	495	2.37	1.09	2	2.38	1.48	0	5	5	-0.14	-0.17	0.05
fa08	8	495	2.21	1.14	2	2.17	1.48	0	5	5	0.28	-0.13	0.05
fa09	9	495	2.29	1.18	2	2.27	1.48	0	5	5	0.15	-0.40	0.05
fa10	10	495	2.64	1.20	3	2.62	1.48	0	5	5	0.06	-0.28	0.05

taia %>% select(all_of(fa_items_0)) %>% 
  pivot_longer(cols = all_of(fa_items_0)) %>% 
  ggplot(aes(value)) +
  geom_bar(fill = "darkgreen") +
  facet_wrap(~ name) +
  scale_x_discrete(limits = 0:5) +
  labs(x = "Score", y = "Number of observations",
       title = "Faith") +
  theme(plot.title = element_text(hjust = .5))

## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?

• fa01 OK
  • Я готов (готова) довериться интеллектуальным системам, даже если я не до конца понимаю, как они работают
• fa02 light positive skewness
  • Чтобы доверять интеллектуальной системе, мне нужно точно понимать, как работают её алгоритмы (R)
• fa03 hard positive skewness
  • Если интеллектуальная система перестает реагировать на запросы, мне с этим комфортно
• fa04 hard positive skewness
  • Если интеллектуальная система управлениями финансами не реагирует на запросы, я все равно буду уверен, что она работает корректно
• fa05 OK
  • Я готов (готова) доверять результатам работы интеллектуальных систем, даже если я не знаю, как они работают
• fa06 OK
  • Я готов (готова) довериться работе интеллектуальных систем так же, как работе профессионалов
• fa07 OK
  • Я предпочту сам (сама) контролировать весь процесс нежели дам контроль интеллектуальной системе (R)
• fa08 light positive skewness
  • Мне нужно знать детали работы алгоритма, чтобы быть уверенным (уверенной) в качестве результата его работы (R)
• fa09 OK
  • Если я не понимаю, как работает интеллектуальная система, я не могу быть уверенным (уверенной) в результате её работы (R)
• fa10 OK
  • Мне не важно, как работает интеллектуальная система, если она должным образом справляется со своей задачей

Dependability

taia %>% 
  select(all_of(de_items_0)) %>% 
  describe() %>% 
  kable(caption = "Dependability", label = 5, digits = 2, col.names = col_names)

Dependability

		Num. of obs.	Mean	SD	Median	Trimmed Mean	MAD	Min	Max	Range	Skewness	Kurtuosis	SE
de01	1	495	2.59	1.10	3	2.64	1.48	0	5	5	-0.42	0.08	0.05
de02	2	495	2.17	1.15	2	2.18	1.48	0	5	5	0.00	-0.34	0.05
de03	3	495	2.17	1.19	2	2.17	1.48	0	5	5	0.04	-0.30	0.05
de04	4	495	1.90	1.05	2	1.85	1.48	0	5	5	0.55	0.66	0.05
de05	5	495	3.57	1.16	4	3.68	1.48	0	5	5	-0.78	0.48	0.05
de06	6	495	2.23	1.23	2	2.25	1.48	0	5	5	0.01	-0.43	0.06
de07	7	495	2.82	1.00	3	2.86	1.48	0	5	5	-0.30	0.31	0.04
de08	8	495	2.65	1.06	3	2.70	1.48	0	5	5	-0.40	0.14	0.05
de09	9	495	3.44	1.20	4	3.54	1.48	0	5	5	-0.61	-0.14	0.05
de10	10	495	2.25	1.18	2	2.28	1.48	0	5	5	-0.22	-0.42	0.05
de11	11	495	2.31	1.20	2	2.31	1.48	0	5	5	0.00	-0.52	0.05

taia %>% select(all_of(de_items_0)) %>% 
  pivot_longer(cols = all_of(de_items_0)) %>% 
  ggplot(aes(value)) +
  geom_bar(fill = "darkblue") +
  facet_wrap(~ name) +
  scale_x_discrete(limits = 0:5) +
  labs(x = "Score", y = "Number of observations",
       title = "Dependability") +
  theme(plot.title = element_text(hjust = .5))

## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?

• de01 negative skewness
  • Я готов (готова) следовать рекомендациям рекомендательных систем социальных сетей
• de02 OK
  • Я готов (готова) делегировать управление финансами интеллектуальному помощнику
• de03 OK
  • Если интеллектуальная система запрашивает мои личные данные, я могу быть уверен (уверенна) в их сохранности
• de04 hard positive skewness
  • Я могу доверять интеллектуальной системе, если я точно понимаю, какие опасности исходят от неё
• de05 hard negative skewness
  • Я бы мог (могла) жить в «умном доме»
• de06 negative kurtosis
  • Если бы я ехал (ехала) в автомобиле, управляемом искусственным интеллектом, я бы был спокоен за свою безопасность
• de07 OK
  • Если меня обследуют с помощью интеллектуальных медицинских технологий, я могу доверять выставленному диагнозу
• de08 OK
  • Я считаю, что при покупках с интеллектуальными помощниками риск ошибиться меньше, чем без них
• de09 negative skewness
  • Мне было бы некомфортно жить в «умном доме» (R)
• de10 negative kurtosis
  • Я могу доверить искусственному интеллекту задачи, от которых зависит моя личная безопасность
• de11 negative kurtosis
  • Я могу доверить искусственному интеллекту только рутинные задачи (например, уборка) (R)

Understanding

taia %>%
  select(all_of(un_items_0)) %>% 
  describe() %>% 
  kable(caption = "Understanding", label = 6, digits = 2, col.names = col_names)

Understanding

		Num. of obs.	Mean	SD	Median	Trimmed Mean	MAD	Min	Max	Range	Skewness	Kurtuosis	SE
un01	1	495	2.93	1.05	3	3.01	1.48	0	5	5	-0.48	0.31	0.05
un02	2	495	2.47	1.14	3	2.49	1.48	0	5	5	-0.17	-0.27	0.05
un03	3	495	3.02	1.17	3	3.10	1.48	0	5	5	-0.55	0.01	0.05
un04	4	495	2.61	1.09	3	2.65	1.48	0	5	5	-0.33	-0.22	0.05
un05	5	495	2.82	1.10	3	2.90	1.48	0	5	5	-0.51	0.24	0.05
un06	6	495	2.29	1.23	2	2.26	1.48	0	5	5	0.19	-0.62	0.06
un07	7	495	2.13	1.18	2	2.14	1.48	0	5	5	-0.02	-0.54	0.05
un08	8	495	2.90	1.16	3	2.96	1.48	0	5	5	-0.45	0.00	0.05
un09	9	495	2.32	1.23	2	2.37	1.48	0	5	5	-0.18	-0.75	0.06
un10	10	495	2.24	1.15	2	2.23	1.48	0	5	5	0.05	-0.44	0.05
un11	11	495	2.63	1.20	3	2.66	1.48	0	5	5	-0.25	-0.37	0.05
un12	12	495	2.89	1.12	3	2.96	1.48	0	5	5	-0.46	0.13	0.05

taia %>% select(all_of(un_items_0)) %>% 
  pivot_longer(cols = all_of(un_items_0)) %>% 
  ggplot(aes(value)) +
  geom_bar(fill = "purple4") +
  facet_wrap(~ name) +
  scale_x_discrete(limits = 0:5) +
  labs(x = "Score", y = "Number of observations",
       title = "Understanding") +
  theme(plot.title = element_text(hjust = .5))

## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?

• un01 OK
  • Я понимаю, как происходит взаимодействие человека с интеллектуальными системами
• un02 OK
  • Я понимаю, как работают алгоритмы интеллектуальных систем
• un03 negative skewness
  • Я стараюсь разобраться в том, как работают интеллектуальные системы
• un04 negative skewness
  • Я понимаю, как работают отдельные элементы интеллектуальных систем
• un05 positive kurtosis
  • Я понимаю общий принцип работы интеллектуальных систем
• un06 negative kurtosis
  • Я плохо разбираюсь в тонкостях работы интеллектуальных систем (R)
• un07 negative kurtosis
  • Мне понятно, как работают интеллектуальные алгоритмы, применяемые в медицинской диагностике
• un08 OK
  • Мне понятно, как работают алгоритмы рекомендательных систем социальных сетей
• un09 extra negative kurtosis
  • Я понимаю, как работают алгоритмы автомобилей, управляемых искусственным интеллектом
• un10 negative kurtosis
  • Я понимаю, как работают алгоритмы интеллектуальных систем, которые управляют финансами
• un11 OK
  • Мне понятно, как устроены алгоритмы машинного перевода текстов
• un12 OK
  • Мне понятно, как работают алгоритмы систем типа «умный дом»

Correlations

Predictability

ggcorrplot(cor(taia %>% select(all_of(pr_items_0))),
           type = "lower", lab = TRUE, lab_size = 3,
           colors = c("indianred1", "white", "royalblue1"),
           title = "Predictability. Interitems correlations",
           show.legend = FALSE)

Consistency

ggcorrplot(cor(taia %>% select(all_of(co_items_0))),
           type = "lower", lab = TRUE, lab_size = 3,
           colors = c("indianred1", "white", "royalblue1"),
           title = "Consistency. Interitems correlations",
           show.legend = FALSE)

Utility

ggcorrplot(cor(taia %>% select(all_of(ut_items_0))),
           type = "lower", lab = TRUE, lab_size = 3,
           colors = c("indianred1", "white", "royalblue1"),
           title = "Utility. Interitems correlations",
           show.legend = FALSE)

Faith

ggcorrplot(cor(taia %>% select(all_of(fa_items_0))),
           type = "lower", lab = TRUE, lab_size = 3,
           colors = c("indianred1", "white", "royalblue1"),
           title = "Faith. Interitems correlations",
           show.legend = FALSE)

Dependability

ggcorrplot(cor(taia %>% select(all_of(de_items_0))),
           type = "lower", lab = TRUE, lab_size = 3,
           colors = c("indianred1", "white", "royalblue1"),
           title = "Dependability. Interitems correlations",
           show.legend = FALSE)

Understanding

ggcorrplot(cor(taia %>% select(all_of(un_items_0))),
           type = "lower", lab = TRUE, lab_size = 3,
           colors = c("indianred1", "white", "royalblue1"),
           title = "Understanding. Interitems correlations",
           show.legend = FALSE)

All TAIA items correlations

ggcorrplot(cor(taia %>% select(all_of(taia_items_0))),
           type = "lower",
           colors = c("indianred1", "white", "royalblue1"),
           title = "TAIA. Interitems correlations", tl.cex = 5, tl.srt = 90,
           legend.title = "Value")

qgraph::qgraph(
  cor(taia %>% select(all_of(taia_items_0))),
  layout = "spring",
  posCol = "royalblue",
  negCol = "indianred"
)

Psychometric Analysis

Subscales

Predictability

pr1 <- psych::alpha(
  taia %>% select(all_of(pr_items_0)),
  cumulative = TRUE,
  title = "Predictability Factor",
  check.keys = FALSE
)

kable(pr1$total,
      caption = "Perdictability. Subscale statistics", 
      label = 7, digits = 2,
      col.names = total_colnames
      )

Perdictability. Subscale statistics

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Scale Mean	Total Score SD	Median interitem correlation
	0.81	0.81	0.82	0.3	4.25	0.01	27.92	6.23	0.33

pr1$item.stats$mean <- pr1$item.stats$mean / 5
kable(pr1$item.stats,
      caption = "Predictability. Items statistics",
      label = 8, digits = 2,
      col.names = item_stats_colnames)

Predictability. Items statistics

	Num. of Obs.	Discrimination	Std Cor	Cor Overlap Corrected	Cor if drop	Difficulty	SD
pr01	495	0.79	0.80	0.80	0.72	0.57	0.99
pr02	495	0.66	0.66	0.62	0.56	0.55	0.97
pr03	495	0.45	0.45	0.37	0.31	0.58	1.01
pr04	495	0.32	0.32	0.21	0.16	0.57	1.04
pr05	495	0.61	0.59	0.51	0.46	0.44	1.20
pr06	495	0.62	0.62	0.56	0.50	0.61	1.07
pr07	495	0.74	0.73	0.70	0.63	0.52	1.11
pr08	495	0.72	0.73	0.71	0.64	0.61	0.91
pr09	495	0.61	0.62	0.56	0.50	0.58	0.95
pr10	495	0.55	0.56	0.47	0.42	0.57	1.04

pr1$item.stats %>%
  ggplot(aes(x = row.names(pr1$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Predictability. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(pr1$alpha.drop,
      caption = "Predictability. Subscale statistics when item drop",
      label = 9, digits = 2, col.names = alpha_drop_colnames)

Predictability. Subscale statistics when item drop

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Var(r)	Median interitem correlation
pr01	0.76	0.77	0.78	0.27	3.27	0.02	0.02	0.29
pr02	0.78	0.79	0.80	0.29	3.66	0.01	0.03	0.32
pr03	0.81	0.81	0.81	0.32	4.33	0.01	0.03	0.36
pr04	0.82	0.83	0.82	0.35	4.78	0.01	0.02	0.36
pr05	0.79	0.80	0.81	0.30	3.88	0.01	0.03	0.33
pr06	0.79	0.79	0.81	0.30	3.79	0.01	0.03	0.33
pr07	0.77	0.78	0.79	0.28	3.46	0.02	0.02	0.30
pr08	0.77	0.78	0.79	0.28	3.46	0.02	0.02	0.30
pr09	0.79	0.79	0.80	0.30	3.80	0.01	0.03	0.35
pr10	0.80	0.80	0.81	0.31	3.99	0.01	0.03	0.35

kable(pr1$response.freq,
      caption = "Predictability. Non missing response frequency for each item",
      label = 10, digits = 2)

Predictability. Non missing response frequency for each item

	0	1	2	3	4	5	miss
pr01	0.02	0.06	0.24	0.45	0.19	0.04	0
pr02	0.01	0.08	0.28	0.43	0.17	0.03	0
pr03	0.01	0.06	0.26	0.40	0.22	0.05	0
pr04	0.02	0.07	0.27	0.38	0.21	0.05	0
pr05	0.09	0.17	0.33	0.29	0.09	0.03	0
pr06	0.02	0.06	0.20	0.41	0.23	0.08	0
pr07	0.04	0.12	0.28	0.38	0.14	0.04	0
pr08	0.02	0.03	0.16	0.52	0.24	0.04	0
pr09	0.02	0.05	0.18	0.53	0.17	0.04	0
pr10	0.02	0.08	0.21	0.45	0.20	0.04	0

Consistency

co1 <- psych::alpha(
  taia %>% select(all_of(co_items_0)),
  cumulative = TRUE,
  title = "Consistency Factor",
  check.keys = FALSE
)

## Warning in psych::alpha(taia %>% select(all_of(co_items_0)), cumulative = TRUE, : Some items were negatively correlated with the total scale and probably 
## should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option

## Some items ( co07 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option

kable(co1$total,
      caption = "Consistency. Subscale statistics", 
      label = 11, digits = 2,
      col.names = total_colnames)

Consistency. Subscale statistics

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Scale Mean	Total Score SD	Median interitem correlation
	0.76	0.77	0.8	0.25	3.27	0.01	24.03	6.1	0.27

co1$item.stats$mean <- co1$item.stats$mean / 5
kable(co1$item.stats,
      caption = "Consistency. Items statistics",
      label = 12, digits = 2,
      col.names = item_stats_colnames)

Consistency. Items statistics

	Num. of Obs.	Discrimination	Std Cor	Cor Overlap Corrected	Cor if drop	Difficulty	SD
co01	495	0.74	0.74	0.72	0.64	0.50	1.08
co02	495	0.68	0.69	0.65	0.57	0.50	1.04
co03	495	0.55	0.55	0.48	0.41	0.57	1.02
co04	495	0.40	0.41	0.30	0.24	0.69	1.09
co05	495	0.79	0.78	0.79	0.70	0.44	1.11
co06	495	0.65	0.65	0.61	0.53	0.50	1.11
co07	495	-0.03	-0.04	-0.22	-0.22	0.32	1.13
co08	495	0.45	0.46	0.36	0.30	0.38	1.04
co09	495	0.76	0.77	0.76	0.67	0.41	1.07
co10	495	0.67	0.67	0.62	0.56	0.49	1.10

co1$item.stats %>%
  ggplot(aes(x = row.names(co1$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Consistency. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(co1$alpha.drop,
      caption = "Consistency. Subscale statistics when item drop",
      label = 13, digits = 2,
      col.names = alpha_drop_colnames)

Consistency. Subscale statistics when item drop

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Var(r)	Median interitem correlation
co01	0.71	0.72	0.76	0.22	2.52	0.02	0.06	0.26
co02	0.72	0.73	0.77	0.23	2.66	0.02	0.06	0.26
co03	0.74	0.75	0.79	0.25	2.98	0.02	0.07	0.27
co04	0.77	0.77	0.80	0.27	3.36	0.01	0.06	0.35
co05	0.70	0.71	0.75	0.21	2.42	0.02	0.06	0.26
co06	0.73	0.73	0.77	0.23	2.74	0.02	0.06	0.27
co07	0.83	0.82	0.83	0.34	4.70	0.01	0.03	0.36
co08	0.76	0.76	0.79	0.26	3.23	0.02	0.07	0.34
co09	0.71	0.71	0.75	0.22	2.47	0.02	0.06	0.26
co10	0.72	0.73	0.77	0.23	2.69	0.02	0.07	0.26

kable(co1$response.freq,
      caption = "Consistency. Non missing response frequency for each item",
      label = 14, digits = 2)

Consistency. Non missing response frequency for each item

	0	1	2	3	4	5	miss
co01	0.05	0.11	0.31	0.39	0.11	0.03	0
co02	0.03	0.12	0.32	0.38	0.13	0.02	0
co03	0.02	0.07	0.22	0.44	0.21	0.04	0
co04	0.01	0.04	0.10	0.35	0.32	0.18	0
co05	0.06	0.19	0.37	0.27	0.09	0.02	0
co06	0.04	0.14	0.28	0.38	0.13	0.03	0
co07	0.18	0.31	0.32	0.14	0.04	0.02	0
co08	0.08	0.27	0.42	0.16	0.06	0.01	0
co09	0.06	0.24	0.40	0.22	0.06	0.02	0
co10	0.04	0.14	0.33	0.34	0.11	0.03	0

Utility

ut1 <- psych::alpha(
  taia %>% select(all_of(ut_items_0)),
  cumulative = TRUE,
  title = "Utility Factor",
  check.keys = FALSE
)

kable(ut1$total,
      caption = "Utility. Subscale statistics", 
      label = 15, digits = 2,
      col.names = total_colnames)

Utility. Subscale statistics

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Scale Mean	Total Score SD	Median interitem correlation
	0.86	0.86	0.87	0.34	6.17	0.01	38.09	8.44	0.37

ut1$item.stats$mean <- ut1$item.stats$mean / 5
kable(ut1$item.stats,
      caption = "Utility. Items statistics",
      label = 16, digits = 2,
      col.names = item_stats_colnames)

Utility. Items statistics

	Num. of Obs.	Discrimination	Std Cor	Cor Overlap Corrected	Cor if drop	Difficulty	SD
ut01	495	0.77	0.78	0.78	0.71	0.76	1.05
ut02	495	0.82	0.83	0.84	0.77	0.70	1.05
ut03	495	0.57	0.57	0.52	0.47	0.71	1.11
ut04	495	0.51	0.51	0.44	0.40	0.62	1.11
ut05	495	0.69	0.69	0.65	0.61	0.61	1.21
ut06	495	0.76	0.76	0.74	0.70	0.65	1.10
ut07	495	0.63	0.63	0.59	0.54	0.64	1.13
ut08	495	0.65	0.66	0.62	0.57	0.69	1.05
ut09	495	0.68	0.68	0.64	0.59	0.64	1.17
ut10	495	0.17	0.17	0.06	0.04	0.43	1.11
ut11	495	0.56	0.55	0.48	0.45	0.53	1.23
ut12	495	0.71	0.71	0.68	0.64	0.63	1.15

ut1$item.stats %>%
  ggplot(aes(x = row.names(ut1$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Utility. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(ut1$alpha.drop,
      caption = "Utility. Subscale statistics when item drop",
      label = 17, digits = 2,
      col.names = alpha_drop_colnames)

Utility. Subscale statistics when item drop

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Var(r)	Median interitem correlation
ut01	0.84	0.84	0.85	0.32	5.16	0.01	0.03	0.34
ut02	0.83	0.83	0.84	0.31	5.00	0.01	0.03	0.33
ut03	0.85	0.85	0.86	0.35	5.84	0.01	0.04	0.41
ut04	0.86	0.86	0.87	0.36	6.06	0.01	0.03	0.41
ut05	0.84	0.84	0.86	0.33	5.45	0.01	0.04	0.37
ut06	0.84	0.84	0.85	0.32	5.21	0.01	0.03	0.33
ut07	0.85	0.85	0.86	0.34	5.65	0.01	0.04	0.37
ut08	0.85	0.85	0.86	0.34	5.55	0.01	0.03	0.37
ut09	0.84	0.85	0.86	0.33	5.49	0.01	0.03	0.37
ut10	0.88	0.88	0.88	0.40	7.40	0.01	0.01	0.41
ut11	0.85	0.86	0.87	0.35	5.92	0.01	0.04	0.41
ut12	0.84	0.84	0.86	0.33	5.37	0.01	0.03	0.35

kable(ut1$response.freq,
      caption = "Utility. Non missing response frequency for each item",
      label = 18, digits = 2)

Utility. Non missing response frequency for each item

	0	1	2	3	4	5	miss
ut01	0.01	0.01	0.06	0.29	0.34	0.28	0
ut02	0.01	0.02	0.09	0.38	0.30	0.20	0
ut03	0.01	0.04	0.09	0.33	0.31	0.22	0
ut04	0.02	0.08	0.15	0.40	0.27	0.09	0
ut05	0.03	0.06	0.20	0.35	0.23	0.12	0
ut06	0.03	0.03	0.13	0.39	0.30	0.12	0
ut07	0.01	0.05	0.19	0.35	0.26	0.14	0
ut08	0.01	0.03	0.11	0.36	0.34	0.15	0
ut09	0.03	0.05	0.15	0.38	0.25	0.13	0
ut10	0.07	0.19	0.37	0.26	0.09	0.02	0
ut11	0.05	0.12	0.27	0.32	0.18	0.07	0
ut12	0.02	0.07	0.15	0.38	0.26	0.12	0

Faith

fa1 <- psych::alpha(
  taia %>% select(all_of(fa_items_0)),
  cumulative = TRUE,
  title = "Faith Factor",
  check.keys = FALSE
)

kable(fa1$total,
      caption = "Faith. Subscale statistics", 
      label = 19, digits = 2,
      col.names = total_colnames)

Faith. Subscale statistics

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Scale Mean	Total Score SD	Median interitem correlation
	0.77	0.77	0.81	0.25	3.26	0.02	22.1	6.41	0.24

fa1$item.stats$mean <- fa1$item.stats$mean / 5
kable(fa1$item.stats,
      caption = "Faith. Items statistics",
      label = 20, digits = 2,
      col.names = item_stats_colnames)

Faith. Items statistics

	Num. of Obs.	Discrimination	Std Cor	Cor Overlap Corrected	Cor if drop	Difficulty	SD
fa01	495	0.76	0.76	0.76	0.67	0.48	1.10
fa02	495	0.61	0.60	0.56	0.47	0.43	1.18
fa03	495	0.27	0.28	0.16	0.10	0.30	1.13
fa04	495	0.41	0.42	0.33	0.26	0.31	1.08
fa05	495	0.77	0.78	0.78	0.69	0.49	1.10
fa06	495	0.55	0.56	0.50	0.42	0.49	1.08
fa07	495	0.42	0.42	0.31	0.26	0.47	1.09
fa08	495	0.58	0.57	0.52	0.44	0.44	1.14
fa09	495	0.66	0.65	0.62	0.53	0.46	1.18
fa10	495	0.63	0.62	0.57	0.50	0.53	1.20

fa1$item.stats %>%
  ggplot(aes(x = row.names(fa1$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Faith. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(fa1$alpha.drop,
      caption = "Faith. Subscale statistics when item drop",
      label = 21, digits = 2,
      col.names = alpha_drop_colnames)

Faith. Subscale statistics when item drop

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Var(r)	Median interitem correlation
fa01	0.71	0.71	0.76	0.22	2.47	0.02	0.04	0.22
fa02	0.74	0.74	0.78	0.24	2.87	0.02	0.04	0.24
fa03	0.79	0.79	0.82	0.29	3.70	0.01	0.04	0.30
fa04	0.77	0.77	0.81	0.27	3.30	0.02	0.04	0.26
fa05	0.71	0.71	0.76	0.21	2.43	0.02	0.04	0.22
fa06	0.75	0.75	0.79	0.25	2.95	0.02	0.05	0.29
fa07	0.77	0.77	0.81	0.27	3.30	0.02	0.05	0.30
fa08	0.74	0.75	0.79	0.25	2.92	0.02	0.04	0.24
fa09	0.73	0.73	0.78	0.23	2.73	0.02	0.04	0.24
fa10	0.74	0.74	0.79	0.24	2.79	0.02	0.04	0.24

kable(fa1$response.freq,
      caption = "Faith. Non missing response frequency for each item",
      label = 22, digits = 2)

Faith. Non missing response frequency for each item

	0	1	2	3	4	5	miss
fa01	0.04	0.16	0.32	0.33	0.13	0.03	0
fa02	0.08	0.21	0.36	0.21	0.12	0.02	0
fa03	0.19	0.35	0.29	0.11	0.05	0.01	0
fa04	0.16	0.34	0.33	0.12	0.04	0.01	0
fa05	0.05	0.12	0.33	0.34	0.13	0.03	0
fa06	0.05	0.12	0.32	0.38	0.11	0.03	0
fa07	0.05	0.15	0.32	0.35	0.11	0.02	0
fa08	0.05	0.20	0.38	0.23	0.10	0.03	0
fa09	0.06	0.19	0.34	0.25	0.13	0.03	0
fa10	0.04	0.12	0.31	0.32	0.14	0.08	0

Dependability

de1 <- psych::alpha(
  taia %>% select(all_of(de_items_0)),
  cumulative = TRUE,
  title = "Dependability Factor",
  check.keys = FALSE
)

## Warning in psych::alpha(taia %>% select(all_of(de_items_0)), cumulative = TRUE, : Some items were negatively correlated with the total scale and probably 
## should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option

## Some items ( de04 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option

kable(de1$total,
      caption = "Dependability. Subscale statistics", 
      label = 23, digits = 2,
      col.names = total_colnames)

Dependability. Subscale statistics

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Scale Mean	Total Score SD	Median interitem correlation
	0.75	0.75	0.8	0.21	2.96	0.02	28.09	6.74	0.24

de1$item.stats$mean <- de1$item.stats$mean / 5
kable(de1$item.stats,
      caption = "Dependability. Items statistics",
      label = 24, digits = 2,
      col.names = item_stats_colnames)

Dependability. Items statistics

	Num. of Obs.	Discrimination	Std Cor	Cor Overlap Corrected	Cor if drop	Difficulty	SD
de01	495	0.57	0.58	0.52	0.45	0.52	1.10
de02	495	0.72	0.72	0.69	0.61	0.43	1.15
de03	495	0.66	0.66	0.61	0.54	0.43	1.19
de04	495	-0.23	-0.22	-0.39	-0.36	0.38	1.05
de05	495	0.59	0.58	0.56	0.46	0.71	1.16
de06	495	0.67	0.67	0.62	0.55	0.45	1.23
de07	495	0.58	0.60	0.54	0.47	0.56	1.00
de08	495	0.67	0.68	0.66	0.57	0.53	1.06
de09	495	0.45	0.44	0.39	0.30	0.69	1.20
de10	495	0.74	0.74	0.72	0.64	0.45	1.18
de11	495	0.43	0.42	0.30	0.27	0.46	1.20

de1$item.stats %>%
  ggplot(aes(x = row.names(de1$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Dependability. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(de1$alpha.drop,
      caption = "Dependability. Subscale statistics when item drop",
      label = 25, digits = 2,
      col.names = alpha_drop_colnames)

Dependability. Subscale statistics when item drop

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Var(r)	Median interitem correlation
de01	0.73	0.72	0.78	0.21	2.59	0.02	0.07	0.22
de02	0.71	0.70	0.76	0.19	2.32	0.02	0.07	0.22
de03	0.72	0.71	0.77	0.20	2.44	0.02	0.07	0.23
de04	0.82	0.82	0.84	0.31	4.49	0.01	0.02	0.32
de05	0.73	0.72	0.76	0.21	2.59	0.02	0.07	0.22
de06	0.71	0.71	0.77	0.19	2.42	0.02	0.07	0.22
de07	0.73	0.72	0.78	0.20	2.56	0.02	0.07	0.22
de08	0.71	0.70	0.76	0.19	2.39	0.02	0.06	0.22
de09	0.75	0.74	0.78	0.22	2.88	0.02	0.07	0.30
de10	0.70	0.69	0.76	0.19	2.28	0.02	0.06	0.22
de11	0.75	0.75	0.80	0.23	2.93	0.02	0.08	0.32

kable(de1$response.freq,
      caption = "Dependability. Non missing response frequency for each item",
      label = 26, digits = 2)

Dependability. Non missing response frequency for each item

	0	1	2	3	4	5	miss
de01	0.05	0.11	0.24	0.43	0.14	0.03	0
de02	0.09	0.17	0.36	0.26	0.10	0.02	0
de03	0.10	0.17	0.35	0.27	0.09	0.03	0
de04	0.07	0.26	0.45	0.14	0.05	0.02	0
de05	0.02	0.03	0.10	0.28	0.34	0.23	0
de06	0.10	0.17	0.32	0.28	0.11	0.03	0
de07	0.02	0.06	0.27	0.42	0.20	0.03	0
de08	0.04	0.10	0.24	0.44	0.15	0.03	0
de09	0.01	0.06	0.13	0.27	0.33	0.20	0
de10	0.10	0.15	0.30	0.34	0.10	0.02	0
de11	0.07	0.20	0.28	0.31	0.12	0.03	0

Understanding

un1 <- psych::alpha(
  taia %>% select(all_of(un_items_0)),
  cumulative = TRUE,
  title = "Understanding Factor",
  check.keys = FALSE
)

kable(un1$total,
      caption = "Understanding. Subscale statistics", 
      label = 27, digits = 2,
      col.names = total_colnames)

Understanding. Subscale statistics

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Scale Mean	Total Score SD	Median interitem correlation
	0.92	0.92	0.92	0.5	12	0.01	31.24	10.15	0.51

un1$item.stats$mean <- un1$item.stats$mean / 5
kable(un1$item.stats,
      caption = "Understanding. Items statistics",
      label = 28, digits = 2,
      col.names = item_stats_colnames)

Understanding. Items statistics

	Num. of Obs.	Discrimination	Std Cor	Cor Overlap Corrected	Cor if drop	Difficulty	SD
un01	495	0.75	0.75	0.73	0.70	0.59	1.05
un02	495	0.84	0.84	0.84	0.81	0.49	1.14
un03	495	0.59	0.59	0.54	0.51	0.60	1.17
un04	495	0.76	0.76	0.73	0.71	0.52	1.09
un05	495	0.81	0.81	0.80	0.77	0.56	1.10
un06	495	0.57	0.56	0.50	0.48	0.46	1.23
un07	495	0.72	0.72	0.69	0.66	0.43	1.18
un08	495	0.76	0.76	0.75	0.71	0.58	1.16
un09	495	0.73	0.73	0.69	0.67	0.46	1.23
un10	495	0.75	0.75	0.72	0.69	0.45	1.15
un11	495	0.79	0.79	0.77	0.74	0.53	1.20
un12	495	0.75	0.76	0.73	0.70	0.58	1.12

un1$item.stats %>%
  ggplot(aes(x = row.names(un1$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Understanding. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(un1$alpha.drop,
      caption = "Understanding. Subscale statistics when item drop",
      label = 29, digits = 2,
      col.names = alpha_drop_colnames)

Understanding. Subscale statistics when item drop

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Var(r)	Median interitem correlation
un01	0.91	0.92	0.91	0.50	10.87	0.01	0.01	0.51
un02	0.91	0.91	0.91	0.48	10.26	0.01	0.01	0.50
un03	0.92	0.92	0.92	0.52	12.05	0.01	0.01	0.54
un04	0.91	0.92	0.92	0.50	10.82	0.01	0.01	0.51
un05	0.91	0.91	0.91	0.49	10.46	0.01	0.01	0.50
un06	0.92	0.92	0.92	0.53	12.30	0.01	0.01	0.54
un07	0.92	0.92	0.92	0.50	11.10	0.01	0.01	0.51
un08	0.91	0.92	0.91	0.50	10.80	0.01	0.01	0.51
un09	0.92	0.92	0.92	0.50	11.06	0.01	0.01	0.51
un10	0.91	0.92	0.92	0.50	10.93	0.01	0.01	0.51
un11	0.91	0.91	0.91	0.49	10.62	0.01	0.01	0.50
un12	0.91	0.92	0.92	0.50	10.86	0.01	0.01	0.50

kable(un1$response.freq,
      caption = "Understanding. Non missing response frequency for each item",
      label = 30, digits = 2)

Understanding. Non missing response frequency for each item

	0	1	2	3	4	5	miss
un01	0.02	0.07	0.19	0.43	0.24	0.05	0
un02	0.05	0.14	0.29	0.35	0.14	0.03	0
un03	0.03	0.08	0.16	0.36	0.29	0.07	0
un04	0.03	0.13	0.24	0.40	0.17	0.02	0
un05	0.04	0.08	0.19	0.44	0.21	0.04	0
un06	0.06	0.24	0.28	0.25	0.14	0.04	0
un07	0.09	0.21	0.30	0.29	0.09	0.02	0
un08	0.04	0.09	0.18	0.39	0.23	0.06	0
un09	0.08	0.18	0.25	0.30	0.17	0.01	0
un10	0.06	0.20	0.33	0.27	0.12	0.02	0
un11	0.05	0.13	0.23	0.36	0.18	0.05	0
un12	0.03	0.08	0.19	0.41	0.23	0.06	0

Split half reliabilitie

Predictability

splitHalf(taia %>% select(all_of(pr_items_0)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")

## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(pr_items_0)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.86
## Guttman lambda 6                          =  0.82
## Average split half reliability            =  0.81
## Guttman lambda 3 (alpha)                  =  0.81
## Guttman lambda 2                          =  0.82
## Minimum split half reliability  (beta)    =  0.74
## Average interitem r =  0.3  with median =  0.33

Consistency

splitHalf(taia %>% select(all_of(co_items_0)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")

## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(co_items_0)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.82
## Guttman lambda 6                          =  0.8
## Average split half reliability            =  0.77
## Guttman lambda 3 (alpha)                  =  0.77
## Guttman lambda 2                          =  0.8
## Minimum split half reliability  (beta)    =  0.66
## Average interitem r =  0.25  with median =  0.27

Utility

splitHalf(taia %>% select(all_of(ut_items_0)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")

## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(ut_items_0)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.9
## Guttman lambda 6                          =  0.87
## Average split half reliability            =  0.86
## Guttman lambda 3 (alpha)                  =  0.86
## Guttman lambda 2                          =  0.87
## Minimum split half reliability  (beta)    =  0.82
## Average interitem r =  0.34  with median =  0.37

Faith

splitHalf(taia %>% select(all_of(fa_items_0)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")

## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(fa_items_0)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.86
## Guttman lambda 6                          =  0.81
## Average split half reliability            =  0.77
## Guttman lambda 3 (alpha)                  =  0.77
## Guttman lambda 2                          =  0.79
## Minimum split half reliability  (beta)    =  0.56
## Average interitem r =  0.25  with median =  0.24

Dependability

splitHalf(taia %>% select(all_of(de_items_0)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")

## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(de_items_0)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.85
## Guttman lambda 6                          =  0.8
## Average split half reliability            =  0.74
## Guttman lambda 3 (alpha)                  =  0.75
## Guttman lambda 2                          =  0.78
## Minimum split half reliability  (beta)    =  0.52
## Average interitem r =  0.21  with median =  0.24

Understanding

splitHalf(taia %>% select(all_of(un_items_0)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")

## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(un_items_0)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.94
## Guttman lambda 6                          =  0.92
## Average split half reliability            =  0.92
## Guttman lambda 3 (alpha)                  =  0.92
## Guttman lambda 2                          =  0.92
## Minimum split half reliability  (beta)    =  0.89
## Average interitem r =  0.5  with median =  0.51

TAIA

splitHalf(taia %>% select(all_of(taia_items_0)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")

## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(taia_items_0)), raw = F, 
##     brute = F, n.sample = 100, covar = F, check.keys = F, key = NULL, 
##     use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.96
## Guttman lambda 6                          =  0.96
## Average split half reliability            =  0.93
## Guttman lambda 3 (alpha)                  =  0.93
## Guttman lambda 2                          =  0.94
## Minimum split half reliability  (beta)    =  0.83
## Average interitem r =  0.17  with median =  0.18

Items exclusion. First step

Excluded items: co07, ut10, de04

Reason: negative discrimination

Stems:

• co07 Я могу сказать, что система работает корректно, только протестировав её (R)
• ut10 Мне кажется, при оформлении услуг через интернет можно справиться и без цифровых помощников (R)
• de04 Я могу доверять интеллектуальной системе, если я точно понимаю, какие опасности исходят от неё (R)

pr_items_1 <- pr_items_0
co_items_1 <- co_items_0[-7]
ut_items_1 <- ut_items_0[-10]
fa_items_1 <- fa_items_0
de_items_1 <- de_items_0[-4]
un_items_1 <- un_items_0
taia_items_1 <- c(pr_items_1,
                  co_items_1,
                  ut_items_1,
                  fa_items_1,
                  de_items_1,
                  un_items_1)

Subscales after first step of exclusion

Consistency

co2 <- psych::alpha(
  taia %>% select(all_of(co_items_1)),
  cumulative = TRUE,
  title = "Consistency Factor",
  check.keys = FALSE
)

kable(co2$total,
      caption = "Consistency. Subscale statistics", 
      label = 11, digits = 2,
      col.names = total_colnames)

Consistency. Subscale statistics

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Scale Mean	Total Score SD	Median interitem correlation
	0.83	0.82	0.83	0.34	4.7	0.01	22.44	6.24	0.36

co2$item.stats$mean <- co2$item.stats$mean / 5
kable(co2$item.stats,
      caption = "Consistency. Items statistics",
      label = 12, digits = 2,
      col.names = item_stats_colnames)

Consistency. Items statistics

	Num. of Obs.	Discrimination	Std Cor	Cor Overlap Corrected	Cor if drop	Difficulty	SD
co01	495	0.74	0.74	0.71	0.65	0.50	1.08
co02	495	0.72	0.72	0.67	0.62	0.50	1.04
co03	495	0.56	0.57	0.48	0.43	0.57	1.02
co04	495	0.44	0.43	0.33	0.28	0.69	1.09
co05	495	0.79	0.78	0.78	0.70	0.44	1.11
co06	495	0.68	0.67	0.62	0.56	0.50	1.11
co08	495	0.44	0.44	0.34	0.29	0.38	1.04
co09	495	0.76	0.76	0.75	0.67	0.41	1.07
co10	495	0.68	0.68	0.62	0.57	0.49	1.10

co2$item.stats %>%
  ggplot(aes(x = row.names(co2$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Consistency. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(co2$alpha.drop,
      caption = "Consistency. Subscale statistics when item drop",
      label = 13, digits = 2,
      col.names = alpha_drop_colnames)

Consistency. Subscale statistics when item drop

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Var(r)	Median interitem correlation
co01	0.79	0.79	0.80	0.32	3.81	0.01	0.03	0.34
co02	0.80	0.80	0.81	0.33	3.89	0.01	0.03	0.35
co03	0.82	0.82	0.82	0.36	4.49	0.01	0.03	0.41
co04	0.84	0.83	0.83	0.39	5.04	0.01	0.02	0.41
co05	0.79	0.79	0.79	0.31	3.66	0.01	0.02	0.33
co06	0.80	0.80	0.81	0.34	4.06	0.01	0.03	0.35
co08	0.83	0.83	0.83	0.39	5.01	0.01	0.02	0.40
co09	0.79	0.79	0.80	0.32	3.74	0.01	0.02	0.34
co10	0.80	0.80	0.81	0.34	4.04	0.01	0.03	0.35

kable(co2$response.freq,
      caption = "Consistency. Non missing response frequency for each item",
      label = 14, digits = 2)

Consistency. Non missing response frequency for each item

	0	1	2	3	4	5	miss
co01	0.05	0.11	0.31	0.39	0.11	0.03	0
co02	0.03	0.12	0.32	0.38	0.13	0.02	0
co03	0.02	0.07	0.22	0.44	0.21	0.04	0
co04	0.01	0.04	0.10	0.35	0.32	0.18	0
co05	0.06	0.19	0.37	0.27	0.09	0.02	0
co06	0.04	0.14	0.28	0.38	0.13	0.03	0
co08	0.08	0.27	0.42	0.16	0.06	0.01	0
co09	0.06	0.24	0.40	0.22	0.06	0.02	0
co10	0.04	0.14	0.33	0.34	0.11	0.03	0

Utility

ut2 <- psych::alpha(
  taia %>% select(all_of(ut_items_1)),
  cumulative = TRUE,
  title = "Utility Factor",
  check.keys = FALSE
)

kable(ut2$total,
      caption = "Utility. Subscale statistics", 
      label = 15, digits = 2,
      col.names = total_colnames)

Utility. Subscale statistics

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Scale Mean	Total Score SD	Median interitem correlation
	0.88	0.88	0.88	0.4	7.4	0.01	35.92	8.32	0.41

ut2$item.stats$mean <- ut2$item.stats$mean / 5
kable(ut2$item.stats,
      caption = "Utility. Items statistics",
      label = 16, digits = 2,
      col.names = item_stats_colnames)

Utility. Items statistics

	Num. of Obs.	Discrimination	Std Cor	Cor Overlap Corrected	Cor if drop	Difficulty	SD
ut01	495	0.79	0.79	0.79	0.73	0.76	1.05
ut02	495	0.83	0.84	0.84	0.79	0.70	1.05
ut03	495	0.55	0.56	0.49	0.45	0.71	1.11
ut04	495	0.53	0.53	0.46	0.42	0.62	1.11
ut05	495	0.69	0.68	0.64	0.60	0.61	1.21
ut06	495	0.77	0.77	0.75	0.71	0.65	1.10
ut07	495	0.62	0.62	0.57	0.52	0.64	1.13
ut08	495	0.66	0.67	0.62	0.58	0.69	1.05
ut09	495	0.70	0.70	0.66	0.62	0.64	1.17
ut11	495	0.57	0.56	0.49	0.46	0.53	1.23
ut12	495	0.72	0.72	0.68	0.65	0.63	1.15

ut2$item.stats %>%
  ggplot(aes(x = row.names(ut2$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Utility. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(ut2$alpha.drop,
      caption = "Utility. Subscale statistics when item drop",
      label = 17, digits = 2,
      col.names = alpha_drop_colnames)

Utility. Subscale statistics when item drop

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Var(r)	Median interitem correlation
ut01	0.86	0.86	0.86	0.38	6.21	0.01	0.01	0.40
ut02	0.86	0.86	0.86	0.38	6.01	0.01	0.01	0.37
ut03	0.88	0.88	0.88	0.42	7.30	0.01	0.01	0.43
ut04	0.88	0.88	0.88	0.43	7.41	0.01	0.01	0.43
ut05	0.87	0.87	0.87	0.40	6.69	0.01	0.01	0.41
ut06	0.86	0.86	0.87	0.39	6.30	0.01	0.01	0.41
ut07	0.87	0.87	0.87	0.41	6.99	0.01	0.01	0.42
ut08	0.87	0.87	0.87	0.40	6.77	0.01	0.01	0.41
ut09	0.87	0.87	0.87	0.40	6.64	0.01	0.02	0.41
ut11	0.88	0.88	0.88	0.42	7.27	0.01	0.01	0.44
ut12	0.86	0.87	0.87	0.39	6.52	0.01	0.02	0.41

kable(ut2$response.freq,
      caption = "Utility. Non missing response frequency for each item",
      label = 18, digits = 2)

Utility. Non missing response frequency for each item

	0	1	2	3	4	5	miss
ut01	0.01	0.01	0.06	0.29	0.34	0.28	0
ut02	0.01	0.02	0.09	0.38	0.30	0.20	0
ut03	0.01	0.04	0.09	0.33	0.31	0.22	0
ut04	0.02	0.08	0.15	0.40	0.27	0.09	0
ut05	0.03	0.06	0.20	0.35	0.23	0.12	0
ut06	0.03	0.03	0.13	0.39	0.30	0.12	0
ut07	0.01	0.05	0.19	0.35	0.26	0.14	0
ut08	0.01	0.03	0.11	0.36	0.34	0.15	0
ut09	0.03	0.05	0.15	0.38	0.25	0.13	0
ut11	0.05	0.12	0.27	0.32	0.18	0.07	0
ut12	0.02	0.07	0.15	0.38	0.26	0.12	0

Dependability

de2 <- psych::alpha(
  taia %>% select(all_of(de_items_1)),
  cumulative = TRUE,
  title = "Dependability Factor",
  check.keys = FALSE
)

kable(de2$total,
      caption = "Dependability. Subscale statistics", 
      label = 23, digits = 2,
      col.names = total_colnames)

Dependability. Subscale statistics

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Scale Mean	Total Score SD	Median interitem correlation
	0.82	0.82	0.84	0.31	4.49	0.01	26.19	7.05	0.32

de2$item.stats$mean <- de2$item.stats$mean / 5
kable(de2$item.stats,
      caption = "Dependability. Items statistics",
      label = 24, digits = 2,
      col.names = item_stats_colnames)

Dependability. Items statistics

	Num. of Obs.	Discrimination	Std Cor	Cor Overlap Corrected	Cor if drop	Difficulty	SD
de01	495	0.58	0.59	0.53	0.47	0.52	1.10
de02	495	0.72	0.72	0.68	0.62	0.43	1.15
de03	495	0.65	0.65	0.60	0.54	0.43	1.19
de05	495	0.62	0.62	0.59	0.50	0.71	1.16
de06	495	0.67	0.67	0.62	0.56	0.45	1.23
de07	495	0.61	0.62	0.56	0.51	0.56	1.00
de08	495	0.70	0.71	0.67	0.61	0.53	1.06
de09	495	0.46	0.45	0.40	0.31	0.69	1.20
de10	495	0.73	0.73	0.71	0.64	0.45	1.18
de11	495	0.41	0.40	0.28	0.25	0.46	1.20

de2$item.stats %>%
  ggplot(aes(x = row.names(de2$item.stats))) +
  geom_point(aes(y = mean), color = "darkblue", size = 3) +
  geom_point(aes(y = raw.r), color = "darkred", size = 2) +
  geom_hline(yintercept = 0.1, color = "darkblue") +
  geom_hline(yintercept = 0.9, color = "darkblue") +
  geom_hline(yintercept = 0.25, color = "darkred") +
  geom_hline(yintercept = 0, color = "black") +
  labs(x = "Item", y = "Value",
       title = "Dependability. Items characteristics",
       subtitle = "Difficulty (blue) and Dicrimination (red)") +
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))

kable(de2$alpha.drop,
      caption = "Dependability. Subscale statistics when item drop",
      label = 25, digits = 2,
      col.names = alpha_drop_colnames)

Dependability. Subscale statistics when item drop

	Alpha	Standardized Alpha	Guttman’s Lambda 6	Average interitem correlation	S/N	Alpha SE	Var(r)	Median interitem correlation
de01	0.80	0.80	0.82	0.31	4.11	0.01	0.02	0.31
de02	0.79	0.79	0.81	0.29	3.72	0.01	0.02	0.29
de03	0.79	0.80	0.82	0.30	3.93	0.01	0.02	0.29
de05	0.80	0.80	0.80	0.31	4.04	0.01	0.02	0.35
de06	0.79	0.80	0.81	0.30	3.88	0.01	0.02	0.31
de07	0.80	0.80	0.82	0.31	4.02	0.01	0.02	0.29
de08	0.79	0.79	0.81	0.29	3.75	0.01	0.02	0.29
de09	0.82	0.82	0.82	0.34	4.60	0.01	0.02	0.35
de10	0.78	0.79	0.80	0.29	3.67	0.01	0.02	0.29
de11	0.83	0.83	0.84	0.35	4.79	0.01	0.02	0.37

kable(de2$response.freq,
      caption = "Dependability. Non missing response frequency for each item",
      label = 26, digits = 2)

Dependability. Non missing response frequency for each item

	0	1	2	3	4	5	miss
de01	0.05	0.11	0.24	0.43	0.14	0.03	0
de02	0.09	0.17	0.36	0.26	0.10	0.02	0
de03	0.10	0.17	0.35	0.27	0.09	0.03	0
de05	0.02	0.03	0.10	0.28	0.34	0.23	0
de06	0.10	0.17	0.32	0.28	0.11	0.03	0
de07	0.02	0.06	0.27	0.42	0.20	0.03	0
de08	0.04	0.10	0.24	0.44	0.15	0.03	0
de09	0.01	0.06	0.13	0.27	0.33	0.20	0
de10	0.10	0.15	0.30	0.34	0.10	0.02	0
de11	0.07	0.20	0.28	0.31	0.12	0.03	0

Reliability measures

Consistency

splitHalf(taia %>% select(all_of(co_items_1)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")

## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(co_items_1)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.87
## Guttman lambda 6                          =  0.83
## Average split half reliability            =  0.82
## Guttman lambda 3 (alpha)                  =  0.82
## Guttman lambda 2                          =  0.83
## Minimum split half reliability  (beta)    =  0.72
## Average interitem r =  0.34  with median =  0.36

Utility

splitHalf(taia %>% select(all_of(ut_items_1)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")

## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(ut_items_1)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.91
## Guttman lambda 6                          =  0.88
## Average split half reliability            =  0.87
## Guttman lambda 3 (alpha)                  =  0.88
## Guttman lambda 2                          =  0.88
## Minimum split half reliability  (beta)    =  0.83
## Average interitem r =  0.4  with median =  0.41

Dependability

splitHalf(taia %>% select(all_of(de_items_1)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")

## Split half reliabilities  
## Call: splitHalf(r = taia %>% select(all_of(de_items_1)), raw = F, brute = F, 
##     n.sample = 100, covar = F, check.keys = F, key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.88
## Guttman lambda 6                          =  0.84
## Average split half reliability            =  0.82
## Guttman lambda 3 (alpha)                  =  0.82
## Guttman lambda 2                          =  0.83
## Minimum split half reliability  (beta)    =  0.72
## Average interitem r =  0.31  with median =  0.32

TAIA

splitHalf(taia %>% ungroup() %>% select(all_of(taia_items_1)),
          raw=F, brute=F, n.sample=100, covar=F,
          check.keys=F, key=NULL, use="pairwise")

## Split half reliabilities  
## Call: splitHalf(r = taia %>% ungroup() %>% select(all_of(taia_items_1)), 
##     raw = F, brute = F, n.sample = 100, covar = F, check.keys = F, 
##     key = NULL, use = "pairwise")
## 
## Maximum split half reliability (lambda 4) =  0.96
## Guttman lambda 6                          =  0.97
## Average split half reliability            =  0.94
## Guttman lambda 3 (alpha)                  =  0.94
## Guttman lambda 2                          =  0.94
## Minimum split half reliability  (beta)    =  0.89
## Average interitem r =  0.2  with median =  0.2

Exploratory Factor Analysis

6 factors

varimax rotation

efa_6f_vm <- factanal(
  taia %>% select(all_of(taia_items_1)),
  factors = 6,
  scores = "regression",
  rotation = "varimax"
)

loadings(efa_6f_vm)

## 
## Loadings:
##      Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## pr01  0.489   0.475   0.172           0.287         
## pr02  0.263   0.392   0.219           0.164   0.181 
## pr03  0.219                           0.547         
## pr04         -0.109           0.139   0.437         
## pr05  0.204   0.350   0.139           0.123   0.676 
## pr06  0.475   0.279   0.144                   0.105 
## pr07  0.387   0.525                   0.208   0.134 
## pr08  0.492   0.429   0.125           0.241         
## pr09  0.350   0.347   0.133           0.220         
## pr10  0.328   0.301   0.124           0.108         
## co01  0.243   0.676           0.116                 
## co02  0.169   0.626   0.115                         
## co03  0.416   0.370                   0.159   0.141 
## co04  0.532   0.147   0.121           0.161         
## co05  0.137   0.724                                 
## co06  0.152   0.554          -0.117                 
## co08 -0.118   0.413  -0.115  -0.127  -0.467         
## co09          0.676                  -0.160         
## co10  0.184   0.526   0.206          -0.123         
## ut01  0.810                                         
## ut02  0.806           0.102           0.135   0.125 
## ut03  0.472  -0.102           0.119   0.507         
## ut04  0.461                   0.104           0.132 
## ut05  0.598   0.194                                 
## ut06  0.717   0.164                           0.125 
## ut07  0.550   0.207                                 
## ut08  0.576   0.249                                 
## ut09  0.593   0.174                   0.102   0.128 
## ut11  0.368   0.255   0.158           0.126   0.591 
## ut12  0.604   0.233   0.135           0.142         
## fa01  0.300   0.345           0.628                 
## fa02                 -0.207   0.669   0.148         
## fa03 -0.116   0.385                  -0.318   0.148 
## fa04          0.549           0.103  -0.192   0.177 
## fa05  0.293   0.411           0.622                 
## fa06  0.301   0.573   0.126   0.188   0.182   0.163 
## fa07                          0.196   0.474   0.193 
## fa08                 -0.191   0.602   0.232         
## fa09                 -0.155   0.631   0.310         
## fa10  0.280   0.136           0.599                 
## de01  0.278   0.429   0.162                         
## de02  0.225   0.571   0.129           0.155   0.228 
## de03  0.223   0.475   0.115   0.138           0.214 
## de05  0.508   0.173   0.137           0.385         
## de06  0.218   0.352   0.184   0.171           0.666 
## de07  0.439   0.301   0.220                   0.182 
## de08  0.345   0.412   0.193   0.107   0.185   0.205 
## de09  0.259                           0.592         
## de10  0.287   0.512           0.177           0.351 
## de11                          0.181   0.332   0.255 
## un01  0.287           0.731                         
## un02          0.138   0.815                         
## un03  0.207           0.502  -0.247                 
## un04          0.113   0.711                         
## un05  0.191           0.791                         
## un06 -0.131           0.518           0.189         
## un07          0.286   0.648          -0.139   0.128 
## un08  0.182           0.746                         
## un09          0.162   0.662                   0.197 
## un10          0.261   0.690                         
## un11                  0.763                         
## un12  0.236           0.713                         
## 
##                Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## SS loadings      7.605   7.077   6.534   2.832   2.786   2.029
## Proportion Var   0.123   0.114   0.105   0.046   0.045   0.033
## Cumulative Var   0.123   0.237   0.342   0.388   0.433   0.466

efa_6f_vm$uniquenesses %>% 
  sort(decreasing = TRUE) %>% 
  kable(col.names = "Uniqueness", digits = 2)

	Uniqueness
de11	0.78
pr04	0.77
pr10	0.76
ut04	0.76
fa03	0.71
de01	0.70
fa07	0.69
pr09	0.69
un06	0.68
pr02	0.67
pr06	0.66
ut07	0.65
co04	0.65
co06	0.65
de03	0.65
un03	0.64
pr03	0.64
co03	0.63
co10	0.63
de07	0.62
fa04	0.61
ut08	0.59
ut05	0.59
ut09	0.59
de08	0.59
de09	0.58
co08	0.57
co02	0.56
de05	0.54
fa10	0.54
ut12	0.54
fa08	0.53
de02	0.52
co09	0.51
pr07	0.50
pr08	0.50
ut03	0.49
de10	0.49
un09	0.49
fa09	0.47
fa02	0.47
fa06	0.47
un04	0.46
co01	0.46
un07	0.46
co05	0.45
ut06	0.44
un10	0.44
un12	0.43
pr01	0.41
ut11	0.41
un08	0.41
un11	0.39
fa01	0.38
un01	0.37
fa05	0.35
pr05	0.34
ut01	0.33
un05	0.33
de06	0.31
un02	0.31
ut02	0.30

promax rotation

efa_6f_pm <- factanal(
  taia %>% select(all_of(taia_items_1)),
  factors = 6,
  scores = "regression",
  rotation = "promax"
)

loadings(efa_6f_pm)

## 
## Loadings:
##      Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## pr01  0.461           0.225   0.283                 
## pr02  0.338   0.110           0.148           0.136 
## pr03         -0.110           0.614                 
## pr04 -0.141                   0.484                 
## pr05                                          0.790 
## pr06  0.174           0.410                         
## pr07  0.527           0.131   0.192                 
## pr08  0.422           0.278   0.238                 
## pr09  0.361           0.146   0.227                 
## pr10  0.263           0.204          -0.122         
## co01  0.809                           0.102  -0.145 
## co02  0.765                                  -0.137 
## co03  0.303           0.264   0.114                 
## co04                  0.493   0.121          -0.132 
## co05  0.902  -0.102  -0.117                  -0.109 
## co06  0.668                          -0.154         
## co08  0.542  -0.180          -0.501  -0.100         
## co09  0.817  -0.113  -0.123  -0.169                 
## co10  0.598   0.115          -0.135                 
## ut01 -0.249           0.974                         
## ut02 -0.231           0.910                   0.113 
## ut03 -0.213           0.385   0.515                 
## ut04 -0.198           0.552                   0.146 
## ut05                  0.658  -0.124                 
## ut06                  0.789                   0.108 
## ut07  0.128           0.593                         
## ut08  0.186           0.543                  -0.108 
## ut09                  0.608                   0.110 
## ut11                  0.291                   0.694 
## ut12  0.106           0.559                         
## fa01  0.277   0.125   0.171           0.650         
## fa02                 -0.129           0.695         
## fa03  0.418          -0.154  -0.379           0.158 
## fa04  0.609          -0.225  -0.241           0.146 
## fa05  0.367   0.105   0.156  -0.119   0.648         
## fa06  0.580                   0.149   0.131         
## fa07                 -0.218   0.508   0.109   0.173 
## fa08                 -0.179   0.165   0.604         
## fa09                 -0.187   0.254   0.627         
## fa10                  0.281  -0.127   0.648  -0.119 
## de01  0.460                                         
## de02  0.569                   0.135           0.166 
## de03  0.430                                   0.173 
## de05  0.114           0.340   0.407  -0.107         
## de06                                          0.770 
## de07  0.160   0.109   0.337                   0.156 
## de08  0.329           0.129   0.141           0.153 
## de09                          0.692  -0.101         
## de10  0.397           0.106           0.103   0.339 
## de11         -0.106  -0.103   0.320   0.101   0.273 
## un01 -0.148   0.804   0.225           0.123  -0.141 
## un02          0.857  -0.105                         
## un03          0.451   0.145          -0.247         
## un04          0.734                                 
## un05          0.844                                 
## un06          0.556  -0.347   0.256                 
## un07  0.192   0.641  -0.115  -0.172           0.113 
## un08 -0.114   0.817                   0.113         
## un09          0.646  -0.101                   0.212 
## un10  0.219   0.698  -0.212                         
## un11          0.803          -0.103                 
## un12 -0.180   0.740   0.166                         
## 
##                Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## SS loadings      7.535   6.547   6.389   3.041   2.823   2.419
## Proportion Var   0.122   0.106   0.103   0.049   0.046   0.039
## Cumulative Var   0.122   0.227   0.330   0.379   0.425   0.464

efa_6f_pm$uniquenesses %>% 
  sort(decreasing = TRUE) %>% 
  kable(col.names = "Uniqueness", digits = 2)

	Uniqueness
de11	0.78
pr04	0.77
pr10	0.76
ut04	0.76
fa03	0.71
de01	0.70
fa07	0.69
pr09	0.69
un06	0.68
pr02	0.67
pr06	0.66
ut07	0.65
co04	0.65
co06	0.65
de03	0.65
un03	0.64
pr03	0.64
co03	0.63
co10	0.63
de07	0.62
fa04	0.61
ut08	0.59
ut05	0.59
ut09	0.59
de08	0.59
de09	0.58
co08	0.57
co02	0.56
de05	0.54
fa10	0.54
ut12	0.54
fa08	0.53
de02	0.52
co09	0.51
pr07	0.50
pr08	0.50
ut03	0.49
de10	0.49
un09	0.49
fa09	0.47
fa02	0.47
fa06	0.47
un04	0.46
co01	0.46
un07	0.46
co05	0.45
ut06	0.44
un10	0.44
un12	0.43
pr01	0.41
ut11	0.41
un08	0.41
un11	0.39
fa01	0.38
un01	0.37
fa05	0.35
pr05	0.34
ut01	0.33
un05	0.33
de06	0.31
un02	0.31
ut02	0.30

5 factors

varimax rotation

efa_5f_vm <- factanal(
  taia %>% select(all_of(taia_items_1)),
  factors = 5,
  scores = "regression",
  rotation = "varimax"
)

loadings(efa_5f_vm)

## 
## Loadings:
##      Factor1 Factor2 Factor3 Factor4 Factor5
## pr01  0.576   0.372   0.170   0.212         
## pr02  0.319   0.359   0.221   0.205         
## pr03  0.306  -0.108           0.412   0.121 
## pr04  0.131  -0.197           0.340   0.154 
## pr05  0.211   0.453   0.156   0.482         
## pr06  0.501   0.249   0.142   0.100         
## pr07  0.456   0.465           0.232         
## pr08  0.567   0.334   0.123   0.160         
## pr09  0.424   0.257   0.129   0.129         
## pr10  0.369   0.262   0.124   0.104         
## co01  0.303   0.625                   0.130 
## co02  0.235   0.573   0.114           0.112 
## co03  0.462   0.329           0.176         
## co04  0.573           0.115                 
## co05  0.197   0.690                         
## co06  0.207   0.520                         
## co08 -0.157   0.505  -0.112  -0.326  -0.146 
## co09  0.121   0.681          -0.100         
## co10  0.219   0.513   0.206  -0.125         
## ut01  0.788                                 
## ut02  0.792           0.100   0.136         
## ut03  0.531  -0.225           0.358   0.140 
## ut04  0.437                   0.115         
## ut05  0.586   0.191                         
## ut06  0.713   0.148                         
## ut07  0.559   0.171                         
## ut08  0.612   0.179                         
## ut09  0.601   0.152           0.128         
## ut11  0.362   0.341   0.171   0.431         
## ut12  0.632   0.179   0.131   0.115         
## fa01  0.317   0.338           0.123   0.618 
## fa02                 -0.206   0.202   0.659 
## fa03 -0.148   0.484          -0.103         
## fa04          0.616                         
## fa05  0.305   0.412                   0.604 
## fa06  0.366   0.528   0.127   0.241   0.179 
## fa07                          0.512   0.182 
## fa08                 -0.190   0.284   0.591 
## fa09                 -0.155   0.321   0.632 
## fa10  0.278   0.126                   0.611 
## de01  0.320   0.386   0.159                 
## de02  0.282   0.553   0.132   0.281         
## de03  0.246   0.493   0.117   0.176   0.105 
## de05  0.583           0.133   0.230         
## de06  0.219   0.462   0.197   0.470         
## de07  0.462   0.290   0.220   0.182         
## de08  0.392   0.386   0.195   0.273         
## de09  0.367                   0.374         
## de10  0.300   0.558           0.303   0.121 
## de11                          0.445   0.153 
## un01  0.301           0.727                 
## un02          0.124   0.815                 
## un03  0.221           0.501          -0.243 
## un04  0.107   0.116   0.712                 
## un05  0.219           0.787                 
## un06                  0.519   0.148         
## un07          0.328   0.651                 
## un08  0.195           0.744                 
## un09          0.198   0.667   0.124  -0.110 
## un10          0.272   0.692          -0.101 
## un11          0.107   0.764                 
## un12  0.242           0.712                 
## 
##                Factor1 Factor2 Factor3 Factor4 Factor5
## SS loadings      8.695   6.882   6.542   2.836   2.744
## Proportion Var   0.140   0.111   0.106   0.046   0.044
## Cumulative Var   0.140   0.251   0.357   0.403   0.447

efa_5f_vm$uniquenesses %>% 
  sort(decreasing = TRUE) %>% 
  kable(col.names = "Uniqueness", digits = 2)

	Uniqueness
pr04	0.80
ut04	0.79
de11	0.77
pr10	0.76
fa03	0.73
pr09	0.72
de09	0.71
de01	0.71
pr03	0.71
un06	0.70
fa07	0.69
pr02	0.68
co06	0.67
pr06	0.66
ut07	0.65
co04	0.64
de03	0.64
un03	0.64
co03	0.63
co10	0.63
de07	0.62
ut05	0.61
fa04	0.61
ut09	0.60
co02	0.59
ut08	0.59
de05	0.59
co08	0.58
de08	0.58
ut12	0.54
ut11	0.54
fa10	0.53
fa08	0.53
pr08	0.53
ut03	0.52
de02	0.52
co09	0.51
pr07	0.51
co01	0.49
pr05	0.49
de10	0.49
un09	0.49
fa06	0.48
co05	0.47
fa09	0.47
fa02	0.47
de06	0.47
un04	0.46
un07	0.46
ut06	0.46
pr01	0.45
un10	0.44
un12	0.43
un08	0.40
un11	0.40
fa01	0.39
ut01	0.37
un01	0.37
fa05	0.36
ut02	0.34
un05	0.33
un02	0.31

promax rotation

efa_5f_pm <- factanal(
  taia %>% select(all_of(taia_items_1)),
  factors = 5,
  scores = "regression",
  rotation = "promax"
)

loadings(efa_5f_pm)

## 
## Loadings:
##      Factor1 Factor2 Factor3 Factor4 Factor5
## pr01  0.446   0.279           0.163         
## pr02  0.143   0.296   0.114   0.211         
## pr03  0.233  -0.214  -0.117   0.424         
## pr04         -0.293           0.354   0.107 
## pr05 -0.115   0.392           0.579         
## pr06  0.456   0.170                         
## pr07  0.281   0.412           0.218         
## pr08  0.480   0.250           0.106         
## pr09  0.342   0.189                         
## pr10  0.300   0.206                  -0.112 
## co01  0.146   0.650                   0.122 
## co02          0.599                   0.111 
## co03  0.354   0.265           0.139         
## co04  0.624                                 
## co05          0.759          -0.112         
## co06          0.545                  -0.122 
## co08 -0.181   0.655  -0.178  -0.313  -0.109 
## co09          0.763  -0.105  -0.100         
## co10  0.114   0.531   0.122  -0.157         
## ut01  0.932  -0.173          -0.161         
## ut02  0.859  -0.129                         
## ut03  0.556  -0.375           0.303         
## ut04  0.452                                 
## ut05  0.610   0.111                         
## ut06  0.757                                 
## ut07  0.631   0.114          -0.191         
## ut08  0.655                  -0.102         
## ut09  0.605                                 
## ut11  0.121   0.247           0.486         
## ut12  0.625                                 
## fa01  0.152   0.310   0.125           0.634 
## fa02 -0.203                   0.143   0.681 
## fa03 -0.291   0.581                         
## fa04 -0.281   0.695                         
## fa05  0.129   0.400                   0.620 
## fa06  0.134   0.490           0.229   0.130 
## fa07 -0.104                   0.583   0.102 
## fa08 -0.210                   0.254   0.592 
## fa09 -0.158                   0.280   0.627 
## fa10  0.247   0.110          -0.175   0.648 
## de01  0.200   0.356                         
## de02          0.524           0.319         
## de03          0.478           0.183         
## de05  0.562                   0.179         
## de06 -0.118   0.396           0.550         
## de07  0.346   0.199   0.104   0.156         
## de08  0.192   0.310           0.272         
## de09  0.305  -0.213           0.381         
## de10          0.534           0.332         
## de11                 -0.110   0.509         
## un01  0.258  -0.197   0.810  -0.169   0.132 
## un02                  0.857                 
## un03  0.185           0.450          -0.246 
## un04                  0.739                 
## un05  0.103  -0.121   0.847                 
## un06 -0.250  -0.119   0.556   0.200         
## un07 -0.175   0.266   0.641                 
## un08  0.101  -0.128   0.827  -0.111   0.126 
## un09 -0.166           0.647   0.170         
## un10 -0.202   0.204   0.699                 
## un11                  0.805                 
## un12  0.151  -0.155   0.746                 
## 
##                Factor1 Factor2 Factor3 Factor4 Factor5
## SS loadings      7.635   6.936   6.600   3.174   2.713
## Proportion Var   0.123   0.112   0.106   0.051   0.044
## Cumulative Var   0.123   0.235   0.341   0.393   0.436

efa_5f_pm$uniquenesses %>%
  sort(decreasing = TRUE) %>% 
  kable(col.names = "Uniqueness")

	Uniqueness
pr04	0.8036037
ut04	0.7875168
de11	0.7660851
pr10	0.7648954
fa03	0.7322846
pr09	0.7157810
de09	0.7138430
de01	0.7115636
pr03	0.7071547
un06	0.6984492
fa07	0.6934404
pr02	0.6780781
co06	0.6691958
pr06	0.6557484
ut07	0.6500545
co04	0.6435044
de03	0.6411034
un03	0.6404520
co03	0.6336224
co10	0.6291329
de07	0.6207641
ut05	0.6108805
fa04	0.6096628
ut09	0.5950296
co02	0.5895055
ut08	0.5880908
de05	0.5859234
co08	0.5800904
de08	0.5776240
ut12	0.5381209
ut11	0.5378917
fa10	0.5312032
fa08	0.5256412
pr08	0.5255447
ut03	0.5181711
de02	0.5158859
co09	0.5114014
pr07	0.5096638
co01	0.4937745
pr05	0.4926627
de10	0.4869821
un09	0.4861097
fa06	0.4806029
co05	0.4747549
fa09	0.4734086
fa02	0.4713133
de06	0.4691654
un04	0.4646410
un07	0.4595727
ut06	0.4594009
pr01	0.4496805
un10	0.4361787
un12	0.4325425
un08	0.4008023
un11	0.3971241
fa01	0.3854741
ut01	0.3691461
un01	0.3671937
fa05	0.3613714
ut02	0.3407660
un05	0.3303704
un02	0.3106689

4 factors

varimax rotation

efa_4f_vm <- factanal(
  taia %>% select(all_of(taia_items_1)),
  factors = 4,
  scores = "regression",
  rotation = "varimax"
)

loadings(efa_4f_vm)

## 
## Loadings:
##      Factor1 Factor2 Factor3 Factor4
## pr01  0.587   0.391   0.173   0.154 
## pr02  0.338   0.367   0.225   0.105 
## pr03  0.355                   0.326 
## pr04  0.172  -0.184           0.315 
## pr05  0.275   0.444   0.169   0.249 
## pr06  0.508   0.257   0.143         
## pr07  0.470   0.479           0.161 
## pr08  0.574   0.350   0.123         
## pr09  0.428   0.271   0.129   0.115 
## pr10  0.379   0.267   0.125         
## co01  0.271   0.643                 
## co02  0.204   0.589   0.107         
## co03  0.470   0.344           0.135 
## co04  0.557           0.111         
## co05  0.162   0.700                 
## co06  0.195   0.523          -0.117 
## co08 -0.202   0.492  -0.128  -0.316 
## co09          0.686                 
## co10  0.187   0.519   0.196  -0.125 
## ut01  0.767                         
## ut02  0.795           0.102   0.101 
## ut03  0.569  -0.204           0.312 
## ut04  0.442                   0.118 
## ut05  0.572   0.207                 
## ut06  0.712   0.163                 
## ut07  0.530   0.188                 
## ut08  0.595   0.198                 
## ut09  0.609   0.166                 
## ut11  0.418   0.338   0.182   0.195 
## ut12  0.634   0.196   0.132         
## fa01  0.278   0.374           0.546 
## fa02 -0.114          -0.197   0.674 
## fa03 -0.169   0.479                 
## fa04          0.618                 
## fa05  0.263   0.446           0.521 
## fa06  0.374   0.545   0.131   0.248 
## fa07  0.158                   0.427 
## fa08                 -0.178   0.669 
## fa09                 -0.141   0.719 
## fa10  0.220   0.166           0.481 
## de01  0.308   0.402   0.157         
## de02  0.307   0.559   0.139   0.167 
## de03  0.252   0.503   0.119   0.155 
## de05  0.609           0.140   0.109 
## de06  0.276   0.458   0.209   0.295 
## de07  0.479   0.300   0.223         
## de08  0.415   0.398   0.200   0.194 
## de09  0.414                   0.238 
## de10  0.325   0.565           0.232 
## de11  0.139                   0.366 
## un01  0.278           0.716         
## un02          0.132   0.815         
## un03  0.236           0.501  -0.225 
## un04  0.105   0.121   0.710         
## un05  0.211           0.785         
## un06                  0.527         
## un07          0.328   0.648  -0.110 
## un08  0.178           0.737         
## un09          0.195   0.669         
## un10          0.272   0.690  -0.127 
## un11          0.110   0.762         
## un12  0.244           0.712         
## 
##                Factor1 Factor2 Factor3 Factor4
## SS loadings      8.889   7.151   6.515   3.709
## Proportion Var   0.143   0.115   0.105   0.060
## Cumulative Var   0.143   0.259   0.364   0.424

efa_4f_vm$uniquenesses %>% 
  sort(decreasing = TRUE) %>% 
  kable(col.names = "Uniqueness", digits = 2)

	Uniqueness
de11	0.84
pr04	0.83
fa07	0.79
ut04	0.79
pr10	0.77
de09	0.76
pr03	0.76
fa03	0.74
un06	0.72
pr09	0.71
de01	0.71
fa10	0.69
pr02	0.69
ut07	0.68
co06	0.67
co04	0.66
pr06	0.65
de03	0.65
un03	0.64
co10	0.64
ut11	0.64
pr05	0.64
co03	0.63
de07	0.63
ut05	0.62
fa04	0.61
ut08	0.60
co08	0.60
co02	0.60
ut09	0.59
de05	0.59
de08	0.59
de06	0.58
de02	0.55
ut12	0.54
ut03	0.54
pr08	0.52
de10	0.52
pr07	0.52
fa08	0.51
co09	0.51
un09	0.51
co01	0.50
fa02	0.49
fa06	0.48
fa01	0.48
co05	0.48
un04	0.46
fa09	0.46
ut06	0.46
fa05	0.46
un07	0.46
pr01	0.45
un10	0.43
un12	0.43
un08	0.42
un01	0.41
ut01	0.40
un11	0.40
ut02	0.35
un05	0.34
un02	0.31

promax rotation

efa_4f_pm <- factanal(
  taia %>% select(all_of(taia_items_1)),
  factors = 4,
  scores = "regression",
  rotation = "promax"
)

loadings(efa_4f_pm)

## 
## Loadings:
##      Factor1 Factor2 Factor3 Factor4
## pr01  0.515   0.294                 
## pr02  0.217   0.309   0.150         
## pr03  0.371  -0.195           0.265 
## pr04  0.154  -0.280           0.308 
## pr05          0.406   0.109   0.225 
## pr06  0.516   0.174          -0.122 
## pr07  0.359   0.431                 
## pr08  0.543   0.264                 
## pr09  0.378   0.198                 
## pr10  0.363   0.214          -0.107 
## co01          0.667                 
## co02          0.614                 
## co03  0.411   0.279                 
## co04  0.611                         
## co05          0.774  -0.122         
## co06          0.559          -0.170 
## co08 -0.270   0.651  -0.216  -0.301 
## co09          0.777  -0.122  -0.111 
## co10          0.537          -0.169 
## ut01  0.933  -0.176          -0.188 
## ut02  0.916  -0.128                 
## ut03  0.673  -0.364           0.189 
## ut04  0.489                         
## ut05  0.618   0.113          -0.120 
## ut06  0.809                  -0.146 
## ut07  0.599   0.112  -0.103  -0.159 
## ut08  0.656   0.103          -0.129 
## ut09  0.672                  -0.103 
## ut11  0.306   0.261   0.104   0.124 
## ut12  0.674                         
## fa01          0.321           0.549 
## fa02 -0.314          -0.103   0.777 
## fa03 -0.335   0.587                 
## fa04 -0.302   0.710                 
## fa05          0.410           0.525 
## fa06  0.181   0.511           0.195 
## fa07                          0.437 
## fa08 -0.273                   0.764 
## fa09 -0.219                   0.803 
## fa10          0.116           0.481 
## de01  0.189   0.368                 
## de02  0.118   0.544           0.124 
## de03          0.495           0.123 
## de05  0.665                         
## de06          0.411   0.159   0.280 
## de07  0.425   0.209   0.126         
## de08  0.278   0.327   0.116   0.126 
## de09  0.447  -0.196           0.156 
## de10  0.131   0.553           0.188 
## de11                          0.372 
## un01  0.179  -0.198   0.750         
## un02 -0.107           0.869         
## un03  0.243           0.480  -0.283 
## un04                  0.745         
## un05         -0.122   0.829         
## un06 -0.214  -0.110   0.611   0.106 
## un07 -0.189   0.270   0.659         
## un08         -0.130   0.788         
## un09 -0.110           0.698         
## un10 -0.225   0.207   0.718         
## un11 -0.115           0.813         
## un12  0.142  -0.159   0.744         
## 
##                Factor1 Factor2 Factor3 Factor4
## SS loadings      9.044   7.246   6.725   3.937
## Proportion Var   0.146   0.117   0.108   0.064
## Cumulative Var   0.146   0.263   0.371   0.435

efa_4f_pm$uniquenesses %>% 
  sort(decreasing = TRUE) %>% 
  kable(col.names = "Uniqueness", digits = 2)

	Uniqueness
de11	0.84
pr04	0.83
fa07	0.79
ut04	0.79
pr10	0.77
de09	0.76
pr03	0.76
fa03	0.74
un06	0.72
pr09	0.71
de01	0.71
fa10	0.69
pr02	0.69
ut07	0.68
co06	0.67
co04	0.66
pr06	0.65
de03	0.65
un03	0.64
co10	0.64
ut11	0.64
pr05	0.64
co03	0.63
de07	0.63
ut05	0.62
fa04	0.61
ut08	0.60
co08	0.60
co02	0.60
ut09	0.59
de05	0.59
de08	0.59
de06	0.58
de02	0.55
ut12	0.54
ut03	0.54
pr08	0.52
de10	0.52
pr07	0.52
fa08	0.51
co09	0.51
un09	0.51
co01	0.50
fa02	0.49
fa06	0.48
fa01	0.48
co05	0.48
un04	0.46
fa09	0.46
ut06	0.46
fa05	0.46
un07	0.46
pr01	0.45
un10	0.43
un12	0.43
un08	0.42
un01	0.41
ut01	0.40
un11	0.40
ut02	0.35
un05	0.34
un02	0.31

Confirmatory Factor Analysis

Basic model

Model:

mdl1 <- "
PR =~ pr01 + pr02 + pr03 + pr04 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa03 + fa04 + fa05 + fa06 + fa07 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10 + de11
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
"

CFA model fitting:

model1 <- cfa(mdl1, taia %>% select(all_of(taia_items_1)))

summary(model1)

## lavaan 0.6-8 ended normally after 62 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       139
##                                                       
##   Number of observations                           495
##                                                       
## Model Test User Model:
##                                                       
##   Test statistic                              6326.223
##   Degrees of freedom                              1814
##   P-value (Chi-square)                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   PR =~                                               
##     pr01              1.000                           
##     pr02              0.727    0.055   13.199    0.000
##     pr03              0.386    0.060    6.384    0.000
##     pr04              0.153    0.063    2.431    0.015
##     pr05              0.870    0.068   12.721    0.000
##     pr06              0.808    0.061   13.285    0.000
##     pr07              1.040    0.061   17.095    0.000
##     pr08              0.847    0.050   17.029    0.000
##     pr09              0.714    0.054   13.167    0.000
##     pr10              0.666    0.060   11.105    0.000
##   CO =~                                               
##     co01              1.000                           
##     co02              0.896    0.061   14.596    0.000
##     co03              0.646    0.061   10.618    0.000
##     co04              0.459    0.065    7.065    0.000
##     co05              1.090    0.065   16.652    0.000
##     co06              0.856    0.066   13.052    0.000
##     co08              0.438    0.062    7.047    0.000
##     co09              0.978    0.063   15.527    0.000
##     co10              0.831    0.065   12.804    0.000
##   UT =~                                               
##     ut01              1.000                           
##     ut02              1.083    0.055   19.815    0.000
##     ut03              0.682    0.062   11.048    0.000
##     ut04              0.636    0.062   10.244    0.000
##     ut05              0.965    0.066   14.724    0.000
##     ut06              1.008    0.058   17.348    0.000
##     ut07              0.790    0.062   12.676    0.000
##     ut08              0.807    0.057   14.082    0.000
##     ut09              0.945    0.063   14.930    0.000
##     ut11              0.789    0.068   11.527    0.000
##     ut12              0.974    0.062   15.791    0.000
##   FA =~                                               
##     fa01              1.000                           
##     fa02              0.535    0.060    8.869    0.000
##     fa03              0.219    0.060    3.670    0.000
##     fa04              0.424    0.056    7.575    0.000
##     fa05              1.028    0.051   20.280    0.000
##     fa06              0.716    0.053   13.558    0.000
##     fa07              0.335    0.057    5.911    0.000
##     fa08              0.487    0.058    8.366    0.000
##     fa09              0.626    0.059   10.519    0.000
##     fa10              0.785    0.059   13.357    0.000
##   DE =~                                               
##     de01              1.000                           
##     de02              1.315    0.113   11.688    0.000
##     de03              1.183    0.111   10.668    0.000
##     de05              0.981    0.103    9.509    0.000
##     de06              1.276    0.116   11.019    0.000
##     de07              0.979    0.093   10.586    0.000
##     de08              1.185    0.102   11.579    0.000
##     de09              0.604    0.098    6.189    0.000
##     de10              1.377    0.116   11.853    0.000
##     de11              0.476    0.096    4.958    0.000
##   UN =~                                               
##     un01              1.000                           
##     un02              1.215    0.064   18.860    0.000
##     un03              0.817    0.068   11.959    0.000
##     un04              1.025    0.062   16.485    0.000
##     un05              1.142    0.063   18.233    0.000
##     un06              0.783    0.072   10.830    0.000
##     un07              1.040    0.068   15.336    0.000
##     un08              1.120    0.066   16.897    0.000
##     un09              1.090    0.071   15.404    0.000
##     un10              1.050    0.066   15.883    0.000
##     un11              1.192    0.069   17.382    0.000
##     un12              1.065    0.064   16.524    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   PR ~~                                               
##     CO                0.417    0.043    9.702    0.000
##     UT                0.479    0.045   10.634    0.000
##     FA                0.402    0.044    9.066    0.000
##     DE                0.423    0.045    9.495    0.000
##     UN                0.232    0.034    6.779    0.000
##   CO ~~                                               
##     UT                0.280    0.038    7.332    0.000
##     FA                0.355    0.044    8.018    0.000
##     DE                0.326    0.039    8.350    0.000
##     UN                0.171    0.033    5.125    0.000
##   UT ~~                                               
##     FA                0.331    0.043    7.706    0.000
##     DE                0.341    0.040    8.621    0.000
##     UN                0.187    0.034    5.560    0.000
##   FA ~~                                               
##     DE                0.366    0.043    8.534    0.000
##     UN                0.061    0.035    1.738    0.082
##   DE ~~                                               
##     UN                0.186    0.029    6.327    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .pr01              0.366    0.028   13.060    0.000
##    .pr02              0.619    0.041   14.916    0.000
##    .pr03              0.936    0.060   15.582    0.000
##    .pr04              1.071    0.068   15.711    0.000
##    .pr05              0.977    0.065   14.993    0.000
##    .pr06              0.751    0.050   14.902    0.000
##    .pr07              0.581    0.042   13.928    0.000
##    .pr08              0.390    0.028   13.953    0.000
##    .pr09              0.601    0.040   14.921    0.000
##    .pr10              0.805    0.053   15.208    0.000
##    .co01              0.526    0.040   13.135    0.000
##    .co02              0.573    0.041   13.824    0.000
##    .co03              0.780    0.052   15.010    0.000
##    .co04              1.045    0.068   15.460    0.000
##    .co05              0.481    0.039   12.355    0.000
##    .co06              0.763    0.053   14.429    0.000
##    .co08              0.957    0.062   15.462    0.000
##    .co09              0.536    0.040   13.296    0.000
##    .co10              0.763    0.053   14.505    0.000
##    .ut01              0.443    0.033   13.514    0.000
##    .ut02              0.333    0.027   12.266    0.000
##    .ut03              0.918    0.060   15.239    0.000
##    .ut04              0.959    0.063   15.322    0.000
##    .ut05              0.844    0.058   14.654    0.000
##    .ut06              0.528    0.038   13.844    0.000
##    .ut07              0.866    0.058   15.029    0.000
##    .ut08              0.674    0.046   14.788    0.000
##    .ut09              0.776    0.053   14.606    0.000
##    .ut11              1.107    0.073   15.183    0.000
##    .ut12              0.689    0.048   14.384    0.000
##    .fa01              0.393    0.036   11.016    0.000
##    .fa02              1.156    0.076   15.296    0.000
##    .fa03              1.245    0.079   15.664    0.000
##    .fa04              1.028    0.067   15.424    0.000
##    .fa05              0.348    0.034   10.153    0.000
##    .fa06              0.744    0.051   14.506    0.000
##    .fa07              1.092    0.070   15.551    0.000
##    .fa08              1.094    0.071   15.349    0.000
##    .fa09              1.071    0.071   15.086    0.000
##    .fa10              0.932    0.064   14.555    0.000
##    .de01              0.822    0.055   14.942    0.000
##    .de02              0.678    0.048   14.054    0.000
##    .de03              0.895    0.061   14.713    0.000
##    .de05              0.985    0.065   15.098    0.000
##    .de06              0.891    0.061   14.538    0.000
##    .de07              0.636    0.043   14.749    0.000
##    .de08              0.583    0.041   14.151    0.000
##    .de09              1.297    0.083   15.551    0.000
##    .de10              0.678    0.049   13.889    0.000
##    .de11              1.361    0.087   15.625    0.000
##    .un01              0.500    0.035   14.388    0.000
##    .un02              0.399    0.030   13.239    0.000
##    .un03              0.962    0.063   15.267    0.000
##    .un04              0.541    0.037   14.425    0.000
##    .un05              0.425    0.031   13.665    0.000
##    .un06              1.147    0.075   15.374    0.000
##    .un07              0.729    0.049   14.737    0.000
##    .un08              0.583    0.041   14.285    0.000
##    .un09              0.789    0.054   14.721    0.000
##    .un10              0.655    0.045   14.601    0.000
##    .un11              0.583    0.041   14.094    0.000
##    .un12              0.578    0.040   14.413    0.000
##     PR                0.605    0.059   10.207    0.000
##     CO                0.633    0.069    9.144    0.000
##     UT                0.659    0.066    9.942    0.000
##     FA                0.816    0.077   10.599    0.000
##     DE                0.377    0.058    6.490    0.000
##     UN                0.604    0.064    9.418    0.000

Fit measures:

tibble(
  `Model 1` = c(
    "Chi-Squared",
    "DF",
    "p",
    "GFI",
    "AGFI",
    "CFI",
    "TLI",
    "SRMR",
    "RMSEA"
  ),
  Value = round(fitmeasures(
    model1,
    c(
      "chisq",
      "df",
      "pvalue",
      "gfi",
      "agfi",
      "cfi",
      "tli",
      "srmr",
      "rmsea"
    )
  ), 4)
) %>% 
    kable()

Model 1	Value
Chi-Squared	6326.2235
DF	1814.0000
p	0.0000
GFI	0.6381
AGFI	0.6104
CFI	0.7096
TLI	0.6973
SRMR	0.1010
RMSEA	0.0709

Standardized solution:

smodel1 <- standardizedsolution(model1)

Loadings:

smodel1 %>%
  filter(op == "=~") %>%
  kable(
    col.names = c(
      "Factor",
      "",
      "Item",
      "Loading",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )

Factor		Item	Loading	SE	z	p	CI lower bound	CI upper bound
PR	=~	pr01	0.789	0.020	39.913	0.000	0.751	0.828
PR	=~	pr02	0.584	0.032	18.261	0.000	0.521	0.647
PR	=~	pr03	0.296	0.043	6.854	0.000	0.211	0.381
PR	=~	pr04	0.114	0.047	2.456	0.014	0.023	0.206
PR	=~	pr05	0.565	0.033	17.164	0.000	0.501	0.630
PR	=~	pr06	0.587	0.032	18.467	0.000	0.525	0.650
PR	=~	pr07	0.728	0.024	30.723	0.000	0.682	0.775
PR	=~	pr08	0.726	0.024	30.439	0.000	0.679	0.773
PR	=~	pr09	0.583	0.032	18.186	0.000	0.520	0.645
PR	=~	pr10	0.500	0.036	13.888	0.000	0.429	0.570
CO	=~	co01	0.739	0.024	30.666	0.000	0.692	0.786
CO	=~	co02	0.686	0.027	25.127	0.000	0.632	0.739
CO	=~	co03	0.503	0.037	13.698	0.000	0.431	0.575
CO	=~	co04	0.337	0.043	7.853	0.000	0.253	0.421
CO	=~	co05	0.781	0.022	36.274	0.000	0.739	0.823
CO	=~	co06	0.615	0.031	19.688	0.000	0.554	0.676
CO	=~	co08	0.336	0.043	7.829	0.000	0.252	0.420
CO	=~	co09	0.728	0.025	29.429	0.000	0.680	0.777
CO	=~	co10	0.604	0.032	18.958	0.000	0.541	0.666
UT	=~	ut01	0.773	0.021	37.597	0.000	0.733	0.814
UT	=~	ut02	0.836	0.016	51.142	0.000	0.804	0.868
UT	=~	ut03	0.500	0.036	13.954	0.000	0.430	0.570
UT	=~	ut04	0.466	0.037	12.493	0.000	0.393	0.539
UT	=~	ut05	0.649	0.028	22.945	0.000	0.593	0.704
UT	=~	ut06	0.748	0.022	33.612	0.000	0.704	0.791
UT	=~	ut07	0.568	0.033	17.378	0.000	0.504	0.632
UT	=~	ut08	0.624	0.030	21.010	0.000	0.566	0.682
UT	=~	ut09	0.657	0.028	23.611	0.000	0.602	0.711
UT	=~	ut11	0.520	0.035	14.890	0.000	0.452	0.589
UT	=~	ut12	0.690	0.026	26.662	0.000	0.639	0.741
FA	=~	fa01	0.822	0.019	42.299	0.000	0.784	0.860
FA	=~	fa02	0.410	0.040	10.123	0.000	0.330	0.489
FA	=~	fa03	0.175	0.047	3.752	0.000	0.083	0.266
FA	=~	fa04	0.353	0.042	8.337	0.000	0.270	0.436
FA	=~	fa05	0.844	0.018	46.444	0.000	0.809	0.880
FA	=~	fa06	0.600	0.032	18.680	0.000	0.537	0.663
FA	=~	fa07	0.279	0.044	6.264	0.000	0.191	0.366
FA	=~	fa08	0.388	0.041	9.407	0.000	0.307	0.469
FA	=~	fa09	0.479	0.038	12.693	0.000	0.405	0.553
FA	=~	fa10	0.592	0.033	18.218	0.000	0.528	0.656
DE	=~	de01	0.561	0.033	16.808	0.000	0.495	0.626
DE	=~	de02	0.700	0.026	27.265	0.000	0.650	0.751
DE	=~	de03	0.609	0.031	19.717	0.000	0.548	0.670
DE	=~	de05	0.519	0.035	14.677	0.000	0.450	0.588
DE	=~	de06	0.639	0.029	21.837	0.000	0.582	0.696
DE	=~	de07	0.602	0.031	19.273	0.000	0.541	0.663
DE	=~	de08	0.690	0.026	26.215	0.000	0.638	0.741
DE	=~	de09	0.310	0.043	7.191	0.000	0.225	0.394
DE	=~	de10	0.716	0.025	29.002	0.000	0.668	0.765
DE	=~	de11	0.243	0.045	5.436	0.000	0.155	0.331
UN	=~	un01	0.740	0.022	33.462	0.000	0.696	0.783
UN	=~	un02	0.831	0.016	52.491	0.000	0.800	0.862
UN	=~	un03	0.544	0.033	16.332	0.000	0.478	0.609
UN	=~	un04	0.735	0.022	32.798	0.000	0.691	0.779
UN	=~	un05	0.806	0.018	45.760	0.000	0.771	0.840
UN	=~	un06	0.494	0.036	13.895	0.000	0.425	0.564
UN	=~	un07	0.687	0.025	27.059	0.000	0.638	0.737
UN	=~	un08	0.752	0.021	35.289	0.000	0.710	0.794
UN	=~	un09	0.690	0.025	27.358	0.000	0.641	0.740
UN	=~	un10	0.710	0.024	29.592	0.000	0.663	0.757
UN	=~	un11	0.772	0.020	38.620	0.000	0.732	0.811
UN	=~	un12	0.737	0.022	33.023	0.000	0.693	0.780

Covariances:

smodel1 %>%
  filter(op == "~~" & lhs != rhs) %>%
  kable(
    col.names = c(
      "Factor",
      "",
      "Factor",
      "Covariance",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )

Factor		Factor	Covariance	SE	z	p	CI lower bound	CI upper bound
PR	~~	CO	0.674	0.032	21.123	0.000	0.611	0.736
PR	~~	UT	0.758	0.025	29.745	0.000	0.708	0.808
PR	~~	FA	0.572	0.037	15.251	0.000	0.498	0.645
PR	~~	DE	0.886	0.019	47.788	0.000	0.849	0.922
PR	~~	UN	0.383	0.043	8.811	0.000	0.298	0.469
CO	~~	UT	0.434	0.042	10.255	0.000	0.351	0.517
CO	~~	FA	0.493	0.041	12.022	0.000	0.413	0.574
CO	~~	DE	0.667	0.033	20.416	0.000	0.603	0.731
CO	~~	UN	0.276	0.047	5.929	0.000	0.185	0.367
UT	~~	FA	0.451	0.042	10.813	0.000	0.369	0.532
UT	~~	DE	0.684	0.030	22.435	0.000	0.624	0.744
UT	~~	UN	0.296	0.045	6.570	0.000	0.207	0.384
FA	~~	DE	0.659	0.033	19.845	0.000	0.594	0.724
FA	~~	UN	0.087	0.050	1.762	0.078	-0.010	0.185
DE	~~	UN	0.389	0.044	8.920	0.000	0.304	0.475

Residuals:

smodel1 %>%
  filter(op == "~~" & lhs == rhs) %>%
  select(-(2:3)) %>%
  kable(
    col.names = c(
      "Item",
      "Residual",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )

Item	Residual	SE	z	p	CI lower bound	CI upper bound
pr01	0.377	0.031	12.076	0	0.316	0.438
pr02	0.659	0.037	17.653	0	0.586	0.732
pr03	0.912	0.026	35.640	0	0.862	0.962
pr04	0.987	0.011	92.749	0	0.966	1.008
pr05	0.681	0.037	18.291	0	0.608	0.754
pr06	0.655	0.037	17.542	0	0.582	0.728
pr07	0.470	0.035	13.609	0	0.402	0.537
pr08	0.473	0.035	13.667	0	0.405	0.541
pr09	0.661	0.037	17.694	0	0.587	0.734
pr10	0.750	0.036	20.839	0	0.680	0.821
co01	0.454	0.036	12.730	0	0.384	0.523
co02	0.530	0.037	14.153	0	0.456	0.603
co03	0.747	0.037	20.238	0	0.675	0.820
co04	0.887	0.029	30.728	0	0.830	0.943
co05	0.390	0.034	11.593	0	0.324	0.456
co06	0.622	0.038	16.181	0	0.546	0.697
co08	0.887	0.029	30.806	0	0.831	0.944
co09	0.469	0.036	13.014	0	0.399	0.540
co10	0.636	0.038	16.536	0	0.560	0.711
ut01	0.402	0.032	12.630	0	0.340	0.464
ut02	0.301	0.027	11.006	0	0.247	0.354
ut03	0.750	0.036	20.907	0	0.679	0.820
ut04	0.783	0.035	22.500	0	0.715	0.851
ut05	0.579	0.037	15.774	0	0.507	0.651
ut06	0.441	0.033	13.251	0	0.376	0.506
ut07	0.678	0.037	18.290	0	0.605	0.751
ut08	0.611	0.037	16.492	0	0.538	0.683
ut09	0.568	0.037	15.554	0	0.497	0.640
ut11	0.729	0.036	20.063	0	0.658	0.801
ut12	0.524	0.036	14.683	0	0.454	0.594
fa01	0.325	0.032	10.177	0	0.262	0.387
fa02	0.832	0.033	25.070	0	0.767	0.897
fa03	0.969	0.016	59.581	0	0.938	1.001
fa04	0.875	0.030	29.210	0	0.816	0.934
fa05	0.287	0.031	9.357	0	0.227	0.347
fa06	0.640	0.039	16.624	0	0.565	0.716
fa07	0.922	0.025	37.218	0	0.874	0.971
fa08	0.849	0.032	26.527	0	0.787	0.912
fa09	0.770	0.036	21.268	0	0.699	0.841
fa10	0.649	0.038	16.872	0	0.574	0.725
de01	0.686	0.037	18.318	0	0.612	0.759
de02	0.510	0.036	14.162	0	0.439	0.580
de03	0.629	0.038	16.722	0	0.555	0.703
de05	0.731	0.037	19.910	0	0.659	0.803
de06	0.592	0.037	15.832	0	0.519	0.665
de07	0.637	0.038	16.934	0	0.564	0.711
de08	0.524	0.036	14.436	0	0.453	0.595
de09	0.904	0.027	33.919	0	0.852	0.956
de10	0.487	0.035	13.746	0	0.417	0.556
de11	0.941	0.022	43.330	0	0.898	0.984
un01	0.453	0.033	13.854	0	0.389	0.517
un02	0.309	0.026	11.747	0	0.258	0.361
un03	0.705	0.036	19.471	0	0.634	0.775
un04	0.460	0.033	13.962	0	0.395	0.524
un05	0.350	0.028	12.343	0	0.295	0.406
un06	0.756	0.035	21.495	0	0.687	0.825
un07	0.528	0.035	15.106	0	0.459	0.596
un08	0.435	0.032	13.574	0	0.372	0.498
un09	0.524	0.035	15.035	0	0.455	0.592
un10	0.496	0.034	14.549	0	0.429	0.563
un11	0.405	0.031	13.125	0	0.344	0.465
un12	0.458	0.033	13.925	0	0.393	0.522
PR	1.000	0.000	NA	NA	1.000	1.000
CO	1.000	0.000	NA	NA	1.000	1.000
UT	1.000	0.000	NA	NA	1.000	1.000
FA	1.000	0.000	NA	NA	1.000	1.000
DE	1.000	0.000	NA	NA	1.000	1.000
UN	1.000	0.000	NA	NA	1.000	1.000

Visualization:

semPaths(model1,
         what = "std",
         whatLabels = "est",
         style = "lisrel",
         residScale = 10,
         theme = "colorblind",
         rotation = 1,
         layout = "tree",
         cardinal = "lat cov",
         curvePivot = TRUE,
         sizeMan = 3,
         sizeLat = 7)

Model with general factor

Model:

mdl2 <- "
PR =~ pr01 + pr02 + pr03 + pr04 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa03 + fa04 + fa05 + fa06 + fa07 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10 + de11
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
DT =~ PR + CO + UT + FA + DE + UN
"

model2 <- cfa(mdl2, taia %>% select(all_of(taia_items_1)))

summary(model2)

## lavaan 0.6-8 ended normally after 44 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       130
##                                                       
##   Number of observations                           495
##                                                       
## Model Test User Model:
##                                                       
##   Test statistic                              6381.193
##   Degrees of freedom                              1823
##   P-value (Chi-square)                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   PR =~                                               
##     pr01              1.000                           
##     pr02              0.732    0.055   13.227    0.000
##     pr03              0.389    0.061    6.409    0.000
##     pr04              0.160    0.063    2.533    0.011
##     pr05              0.882    0.069   12.845    0.000
##     pr06              0.797    0.061   13.006    0.000
##     pr07              1.049    0.061   17.137    0.000
##     pr08              0.846    0.050   16.877    0.000
##     pr09              0.719    0.055   13.186    0.000
##     pr10              0.661    0.060   10.962    0.000
##   CO =~                                               
##     co01              1.000                           
##     co02              0.894    0.062   14.491    0.000
##     co03              0.652    0.061   10.699    0.000
##     co04              0.473    0.065    7.263    0.000
##     co05              1.088    0.066   16.548    0.000
##     co06              0.862    0.066   13.089    0.000
##     co08              0.435    0.062    6.976    0.000
##     co09              0.977    0.063   15.442    0.000
##     co10              0.834    0.065   12.798    0.000
##   UT =~                                               
##     ut01              1.000                           
##     ut02              1.080    0.055   19.757    0.000
##     ut03              0.674    0.062   10.910    0.000
##     ut04              0.635    0.062   10.237    0.000
##     ut05              0.972    0.065   14.837    0.000
##     ut06              1.007    0.058   17.336    0.000
##     ut07              0.791    0.062   12.699    0.000
##     ut08              0.807    0.057   14.089    0.000
##     ut09              0.946    0.063   14.941    0.000
##     ut11              0.790    0.068   11.534    0.000
##     ut12              0.973    0.062   15.774    0.000
##   FA =~                                               
##     fa01              1.000                           
##     fa02              0.522    0.060    8.760    0.000
##     fa03              0.204    0.059    3.452    0.001
##     fa04              0.407    0.055    7.352    0.000
##     fa05              1.017    0.050   20.447    0.000
##     fa06              0.704    0.052   13.544    0.000
##     fa07              0.323    0.056    5.749    0.000
##     fa08              0.475    0.058    8.239    0.000
##     fa09              0.616    0.059   10.488    0.000
##     fa10              0.780    0.058   13.496    0.000
##   DE =~                                               
##     de01              1.000                           
##     de02              1.314    0.113   11.628    0.000
##     de03              1.176    0.111   10.576    0.000
##     de05              1.006    0.104    9.645    0.000
##     de06              1.267    0.116   10.914    0.000
##     de07              0.989    0.093   10.609    0.000
##     de08              1.188    0.103   11.539    0.000
##     de09              0.623    0.098    6.342    0.000
##     de10              1.371    0.117   11.764    0.000
##     de11              0.475    0.096    4.934    0.000
##   UN =~                                               
##     un01              1.000                           
##     un02              1.212    0.064   18.935    0.000
##     un03              0.811    0.068   11.917    0.000
##     un04              1.022    0.062   16.530    0.000
##     un05              1.140    0.062   18.326    0.000
##     un06              0.780    0.072   10.831    0.000
##     un07              1.035    0.067   15.346    0.000
##     un08              1.118    0.066   16.974    0.000
##     un09              1.085    0.070   15.412    0.000
##     un10              1.046    0.066   15.902    0.000
##     un11              1.188    0.068   17.430    0.000
##     un12              1.061    0.064   16.562    0.000
##   DT =~                                               
##     PR                1.000                           
##     CO                0.746    0.061   12.327    0.000
##     UT                0.818    0.060   13.693    0.000
##     FA                0.781    0.064   12.133    0.000
##     DE                0.777    0.066   11.697    0.000
##     UN                0.408    0.053    7.651    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .pr01              0.369    0.028   13.031    0.000
##    .pr02              0.616    0.041   14.883    0.000
##    .pr03              0.935    0.060   15.576    0.000
##    .pr04              1.070    0.068   15.709    0.000
##    .pr05              0.966    0.065   14.947    0.000
##    .pr06              0.763    0.051   14.921    0.000
##    .pr07              0.573    0.041   13.836    0.000
##    .pr08              0.393    0.028   13.938    0.000
##    .pr09              0.599    0.040   14.890    0.000
##    .pr10              0.810    0.053   15.208    0.000
##    .co01              0.528    0.040   13.119    0.000
##    .co02              0.578    0.042   13.832    0.000
##    .co03              0.775    0.052   14.982    0.000
##    .co04              1.037    0.067   15.438    0.000
##    .co05              0.485    0.039   12.362    0.000
##    .co06              0.759    0.053   14.391    0.000
##    .co08              0.959    0.062   15.463    0.000
##    .co09              0.539    0.041   13.290    0.000
##    .co10              0.761    0.053   14.482    0.000
##    .ut01              0.442    0.033   13.486    0.000
##    .ut02              0.336    0.027   12.276    0.000
##    .ut03              0.925    0.061   15.248    0.000
##    .ut04              0.959    0.063   15.318    0.000
##    .ut05              0.836    0.057   14.615    0.000
##    .ut06              0.528    0.038   13.826    0.000
##    .ut07              0.864    0.058   15.017    0.000
##    .ut08              0.673    0.046   14.776    0.000
##    .ut09              0.775    0.053   14.590    0.000
##    .ut11              1.106    0.073   15.176    0.000
##    .ut12              0.690    0.048   14.373    0.000
##    .fa01              0.375    0.035   10.619    0.000
##    .fa02              1.162    0.076   15.307    0.000
##    .fa03              1.250    0.080   15.672    0.000
##    .fa04              1.036    0.067   15.443    0.000
##    .fa05              0.348    0.035   10.076    0.000
##    .fa06              0.748    0.052   14.517    0.000
##    .fa07              1.097    0.070   15.560    0.000
##    .fa08              1.100    0.072   15.361    0.000
##    .fa09              1.074    0.071   15.090    0.000
##    .fa10              0.927    0.064   14.529    0.000
##    .de01              0.823    0.055   14.925    0.000
##    .de02              0.681    0.049   14.028    0.000
##    .de03              0.904    0.061   14.713    0.000
##    .de05              0.968    0.064   15.039    0.000
##    .de06              0.902    0.062   14.545    0.000
##    .de07              0.630    0.043   14.698    0.000
##    .de08              0.583    0.041   14.109    0.000
##    .de09              1.288    0.083   15.533    0.000
##    .de10              0.687    0.049   13.891    0.000
##    .de11              1.361    0.087   15.623    0.000
##    .un01              0.497    0.035   14.367    0.000
##    .un02              0.399    0.030   13.228    0.000
##    .un03              0.966    0.063   15.272    0.000
##    .un04              0.541    0.038   14.422    0.000
##    .un05              0.423    0.031   13.641    0.000
##    .un06              1.148    0.075   15.374    0.000
##    .un07              0.732    0.050   14.740    0.000
##    .un08              0.581    0.041   14.271    0.000
##    .un09              0.792    0.054   14.725    0.000
##    .un10              0.657    0.045   14.604    0.000
##    .un11              0.584    0.041   14.091    0.000
##    .un12              0.578    0.040   14.412    0.000
##    .PR                0.051    0.017    2.954    0.003
##    .CO                0.324    0.039    8.231    0.000
##    .UT                0.290    0.033    8.734    0.000
##    .FA                0.498    0.051    9.744    0.000
##    .DE                0.043    0.012    3.438    0.001
##    .UN                0.516    0.055    9.336    0.000
##     DT                0.552    0.058    9.531    0.000

tibble(
  `Model 2` = c(
    "Chi-Squared",
    "DF",
    "p",
    "GFI",
    "AGFI",
    "CFI",
    "TLI",
    "SRMR",
    "RMSEA"
  ),
  Value = round(fitmeasures(
    model2,
    c(
      "chisq",
      "df",
      "pvalue",
      "gfi",
      "agfi",
      "cfi",
      "tli",
      "srmr",
      "rmsea"
    )
  ), 4)
) %>% kable()

Model 2	Value
Chi-Squared	6381.1932
DF	1823.0000
p	0.0000
GFI	0.6329
AGFI	0.6068
CFI	0.7067
TLI	0.6957
SRMR	0.1029
RMSEA	0.0711

Standardized solution:

smodel2 <- standardizedsolution(model2)

Loadings:

smodel2 %>%
  filter(op == "=~") %>%
  kable(
    col.names = c(
      "Factor",
      "",
      "Item",
      "Loading",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )

Factor		Item	Loading	SE	z	p	CI lower bound	CI upper bound
PR	=~	pr01	0.787	0.020	39.421	0.00	0.748	0.827
PR	=~	pr02	0.587	0.032	18.392	0.00	0.524	0.649
PR	=~	pr03	0.298	0.043	6.889	0.00	0.213	0.383
PR	=~	pr04	0.119	0.047	2.561	0.01	0.028	0.210
PR	=~	pr05	0.572	0.033	17.497	0.00	0.508	0.636
PR	=~	pr06	0.578	0.032	17.869	0.00	0.515	0.641
PR	=~	pr07	0.732	0.024	31.152	0.00	0.686	0.778
PR	=~	pr08	0.723	0.024	30.023	0.00	0.676	0.770
PR	=~	pr09	0.585	0.032	18.294	0.00	0.522	0.648
PR	=~	pr10	0.495	0.036	13.654	0.00	0.424	0.566
CO	=~	co01	0.738	0.024	30.465	0.00	0.691	0.786
CO	=~	co02	0.683	0.028	24.809	0.00	0.629	0.737
CO	=~	co03	0.507	0.037	13.884	0.00	0.436	0.579
CO	=~	co04	0.346	0.043	8.129	0.00	0.263	0.430
CO	=~	co05	0.779	0.022	35.829	0.00	0.736	0.821
CO	=~	co06	0.618	0.031	19.860	0.00	0.557	0.679
CO	=~	co08	0.333	0.043	7.737	0.00	0.248	0.417
CO	=~	co09	0.727	0.025	29.164	0.00	0.678	0.776
CO	=~	co10	0.605	0.032	18.998	0.00	0.542	0.667
UT	=~	ut01	0.774	0.021	37.585	0.00	0.733	0.814
UT	=~	ut02	0.834	0.017	50.561	0.00	0.802	0.867
UT	=~	ut03	0.495	0.036	13.693	0.00	0.424	0.565
UT	=~	ut04	0.466	0.037	12.481	0.00	0.393	0.539
UT	=~	ut05	0.654	0.028	23.305	0.00	0.599	0.708
UT	=~	ut06	0.748	0.022	33.540	0.00	0.704	0.791
UT	=~	ut07	0.569	0.033	17.431	0.00	0.505	0.633
UT	=~	ut08	0.624	0.030	21.028	0.00	0.566	0.682
UT	=~	ut09	0.658	0.028	23.643	0.00	0.603	0.712
UT	=~	ut11	0.521	0.035	14.903	0.00	0.452	0.589
UT	=~	ut12	0.689	0.026	26.593	0.00	0.639	0.740
FA	=~	fa01	0.831	0.019	43.634	0.00	0.793	0.868
FA	=~	fa02	0.404	0.041	9.937	0.00	0.325	0.484
FA	=~	fa03	0.164	0.047	3.519	0.00	0.073	0.256
FA	=~	fa04	0.343	0.043	8.031	0.00	0.259	0.427
FA	=~	fa05	0.844	0.018	46.107	0.00	0.808	0.880
FA	=~	fa06	0.597	0.032	18.480	0.00	0.533	0.660
FA	=~	fa07	0.271	0.045	6.067	0.00	0.183	0.359
FA	=~	fa08	0.382	0.041	9.209	0.00	0.301	0.463
FA	=~	fa09	0.477	0.038	12.585	0.00	0.403	0.551
FA	=~	fa10	0.595	0.032	18.373	0.00	0.531	0.658
DE	=~	de01	0.560	0.033	16.714	0.00	0.494	0.625
DE	=~	de02	0.699	0.026	26.998	0.00	0.648	0.749
DE	=~	de03	0.604	0.031	19.352	0.00	0.543	0.665
DE	=~	de05	0.531	0.035	15.227	0.00	0.463	0.599
DE	=~	de06	0.633	0.030	21.338	0.00	0.575	0.691
DE	=~	de07	0.607	0.031	19.535	0.00	0.546	0.668
DE	=~	de08	0.690	0.026	26.154	0.00	0.638	0.742
DE	=~	de09	0.319	0.043	7.441	0.00	0.235	0.403
DE	=~	de10	0.712	0.025	28.410	0.00	0.663	0.761
DE	=~	de11	0.242	0.045	5.408	0.00	0.154	0.330
UN	=~	un01	0.742	0.022	33.751	0.00	0.699	0.785
UN	=~	un02	0.831	0.016	52.514	0.00	0.800	0.862
UN	=~	un03	0.541	0.033	16.192	0.00	0.476	0.606
UN	=~	un04	0.735	0.022	32.778	0.00	0.691	0.779
UN	=~	un05	0.807	0.018	46.027	0.00	0.773	0.842
UN	=~	un06	0.494	0.036	13.869	0.00	0.424	0.563
UN	=~	un07	0.686	0.025	26.938	0.00	0.636	0.736
UN	=~	un08	0.753	0.021	35.450	0.00	0.711	0.795
UN	=~	un09	0.689	0.025	27.222	0.00	0.639	0.739
UN	=~	un10	0.709	0.024	29.476	0.00	0.662	0.756
UN	=~	un11	0.771	0.020	38.554	0.00	0.732	0.811
UN	=~	un12	0.736	0.022	32.957	0.00	0.692	0.780
DT	=~	PR	0.957	0.015	65.735	0.00	0.928	0.985
DT	=~	CO	0.697	0.030	23.591	0.00	0.639	0.755
DT	=~	UT	0.748	0.025	29.635	0.00	0.699	0.798
DT	=~	FA	0.635	0.033	19.234	0.00	0.571	0.700
DT	=~	DE	0.941	0.016	60.642	0.00	0.911	0.972
DT	=~	UN	0.389	0.042	9.209	0.00	0.306	0.472

Covariances:

smodel2 %>%
  filter(op == "~~" & lhs != rhs) %>%
  kable(
    col.names = c(
      "Factor",
      "",
      "Factor",
      "Covariance",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )

Factor		Factor	Covariance	SE	z	p	CI lower bound	CI upper bound

Residuals:

smodel2 %>%
  filter(op == "~~" & lhs == rhs) %>%
  select(-(2:3)) %>%
  kable(
    col.names = c(
      "Item",
      "Residual",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )

Item	Residual	SE	z	p	CI lower bound	CI upper bound
pr01	0.380	0.031	12.074	0.000	0.318	0.442
pr02	0.656	0.037	17.528	0.000	0.583	0.729
pr03	0.911	0.026	35.398	0.000	0.861	0.962
pr04	0.986	0.011	88.834	0.000	0.964	1.008
pr05	0.673	0.037	18.033	0.000	0.600	0.747
pr06	0.666	0.037	17.817	0.000	0.593	0.739
pr07	0.464	0.034	13.462	0.000	0.396	0.531
pr08	0.477	0.035	13.693	0.000	0.409	0.545
pr09	0.658	0.037	17.581	0.000	0.584	0.731
pr10	0.755	0.036	21.013	0.000	0.684	0.825
co01	0.455	0.036	12.729	0.000	0.385	0.525
co02	0.534	0.038	14.210	0.000	0.460	0.608
co03	0.743	0.037	20.019	0.000	0.670	0.815
co04	0.880	0.030	29.818	0.000	0.822	0.938
co05	0.393	0.034	11.621	0.000	0.327	0.460
co06	0.618	0.038	16.064	0.000	0.543	0.693
co08	0.889	0.029	31.060	0.000	0.833	0.945
co09	0.472	0.036	13.032	0.000	0.401	0.543
co10	0.634	0.038	16.479	0.000	0.559	0.710
ut01	0.401	0.032	12.604	0.000	0.339	0.464
ut02	0.304	0.028	11.029	0.000	0.250	0.358
ut03	0.755	0.036	21.142	0.000	0.685	0.825
ut04	0.783	0.035	22.492	0.000	0.715	0.851
ut05	0.573	0.037	15.632	0.000	0.501	0.645
ut06	0.441	0.033	13.238	0.000	0.376	0.506
ut07	0.677	0.037	18.236	0.000	0.604	0.749
ut08	0.610	0.037	16.463	0.000	0.538	0.683
ut09	0.568	0.037	15.522	0.000	0.496	0.639
ut11	0.729	0.036	20.030	0.000	0.658	0.800
ut12	0.525	0.036	14.678	0.000	0.455	0.595
fa01	0.310	0.032	9.805	0.000	0.248	0.372
fa02	0.836	0.033	25.404	0.000	0.772	0.901
fa03	0.973	0.015	63.285	0.000	0.943	1.003
fa04	0.882	0.029	30.092	0.000	0.825	0.940
fa05	0.287	0.031	9.297	0.000	0.227	0.348
fa06	0.644	0.039	16.709	0.000	0.568	0.719
fa07	0.927	0.024	38.250	0.000	0.879	0.974
fa08	0.854	0.032	26.951	0.000	0.792	0.916
fa09	0.773	0.036	21.374	0.000	0.702	0.843
fa10	0.646	0.039	16.767	0.000	0.571	0.722
de01	0.687	0.037	18.317	0.000	0.613	0.760
de02	0.512	0.036	14.164	0.000	0.441	0.583
de03	0.635	0.038	16.835	0.000	0.561	0.709
de05	0.718	0.037	19.391	0.000	0.645	0.791
de06	0.599	0.038	15.964	0.000	0.526	0.673
de07	0.632	0.038	16.747	0.000	0.558	0.706
de08	0.524	0.036	14.387	0.000	0.452	0.595
de09	0.898	0.027	32.863	0.000	0.845	0.952
de10	0.493	0.036	13.816	0.000	0.423	0.563
de11	0.941	0.022	43.416	0.000	0.899	0.984
un01	0.450	0.033	13.802	0.000	0.386	0.514
un02	0.309	0.026	11.737	0.000	0.257	0.361
un03	0.707	0.036	19.565	0.000	0.636	0.778
un04	0.460	0.033	13.960	0.000	0.395	0.525
un05	0.349	0.028	12.311	0.000	0.293	0.404
un06	0.756	0.035	21.515	0.000	0.687	0.825
un07	0.529	0.035	15.131	0.000	0.461	0.598
un08	0.433	0.032	13.545	0.000	0.370	0.496
un09	0.525	0.035	15.062	0.000	0.457	0.594
un10	0.497	0.034	14.567	0.000	0.430	0.564
un11	0.405	0.031	13.127	0.000	0.345	0.466
un12	0.458	0.033	13.930	0.000	0.394	0.523
PR	0.084	0.028	3.025	0.002	0.030	0.139
CO	0.514	0.041	12.465	0.000	0.433	0.595
UT	0.440	0.038	11.638	0.000	0.366	0.514
FA	0.596	0.042	14.207	0.000	0.514	0.679
DE	0.114	0.029	3.897	0.000	0.057	0.171
UN	0.849	0.033	25.829	0.000	0.784	0.913
DT	1.000	0.000	NA	NA	1.000	1.000

semPaths(model2,
         what = "std",
         whatLabels = "est",
         style = "lisrel",
         residScale = 10,
         theme = "colorblind",
         rotation = 1,
         layout = "tree",
         cardinal = "lat cov",
         curvePivot = TRUE,
         sizeMan = 3,
         sizeLat = 7)

Items Exclusion. Second step

Excluded items: pr03, pr04, fa03, fa07, de11

Reason: low factor loadings

Stems:

• pr03 Я считаю, что интеллектуальные системы ненадежны (R)
• pr04 Я считаю, что результаты работы интеллектуальных систем невозможно предсказать (R)
• fa03 Если интеллектуальная система перестает реагировать на запросы, мне с этим комфортно
• fa07 Я предпочту сам (сама) контролировать весь процесс нежели дам контроль интеллектуальной системе (R)
• de11 Я могу доверить искусственному интеллекту только рутинные задачи (например, уборка) (R)

pr_items_2 <- pr_items_0[-c(3, 4)]
co_items_2 <- co_items_0[-7]
ut_items_2 <- ut_items_0[-10]
fa_items_2 <- fa_items_0[-c(3, 7)]
de_items_2 <- de_items_0[-c(4, 11)]
un_items_2 <- un_items_0
taia_items_2 <- c(pr_items_2,
                  co_items_2,
                  ut_items_2,
                  fa_items_2,
                  de_items_2,
                  un_items_2)

CFA with new item set

Model:

mdl3 <- "
PR =~ pr01 + pr02 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa04 + fa05 + fa06 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
DT =~ PR + CO + UT + FA + DE + UN
"

model3 <- cfa(mdl3, taia %>% select(all_of(taia_items_2)))

summary(model3)

## lavaan 0.6-8 ended normally after 45 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       120
##                                                       
##   Number of observations                           495
##                                                       
## Model Test User Model:
##                                                       
##   Test statistic                              5261.509
##   Degrees of freedom                              1533
##   P-value (Chi-square)                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   PR =~                                               
##     pr01              1.000                           
##     pr02              0.738    0.056   13.173    0.000
##     pr05              0.890    0.069   12.819    0.000
##     pr06              0.805    0.062   12.996    0.000
##     pr07              1.053    0.062   16.945    0.000
##     pr08              0.852    0.051   16.773    0.000
##     pr09              0.727    0.055   13.194    0.000
##     pr10              0.669    0.061   11.001    0.000
##   CO =~                                               
##     co01              1.000                           
##     co02              0.894    0.062   14.492    0.000
##     co03              0.653    0.061   10.711    0.000
##     co04              0.474    0.065    7.277    0.000
##     co05              1.088    0.066   16.546    0.000
##     co06              0.862    0.066   13.089    0.000
##     co08              0.435    0.062    6.978    0.000
##     co09              0.977    0.063   15.433    0.000
##     co10              0.835    0.065   12.814    0.000
##   UT =~                                               
##     ut01              1.000                           
##     ut02              1.080    0.055   19.757    0.000
##     ut03              0.671    0.062   10.868    0.000
##     ut04              0.635    0.062   10.231    0.000
##     ut05              0.972    0.065   14.846    0.000
##     ut06              1.007    0.058   17.348    0.000
##     ut07              0.792    0.062   12.718    0.000
##     ut08              0.807    0.057   14.098    0.000
##     ut09              0.945    0.063   14.939    0.000
##     ut11              0.789    0.068   11.523    0.000
##     ut12              0.973    0.062   15.779    0.000
##   FA =~                                               
##     fa01              1.000                           
##     fa02              0.516    0.059    8.728    0.000
##     fa04              0.397    0.055    7.215    0.000
##     fa05              1.010    0.049   20.518    0.000
##     fa06              0.691    0.052   13.376    0.000
##     fa08              0.468    0.057    8.178    0.000
##     fa09              0.604    0.058   10.363    0.000
##     fa10              0.785    0.057   13.732    0.000
##   DE =~                                               
##     de01              1.000                           
##     de02              1.310    0.112   11.675    0.000
##     de03              1.170    0.110   10.597    0.000
##     de05              0.998    0.104    9.640    0.000
##     de06              1.255    0.115   10.911    0.000
##     de07              0.988    0.093   10.664    0.000
##     de08              1.187    0.102   11.604    0.000
##     de09              0.603    0.097    6.189    0.000
##     de10              1.363    0.116   11.794    0.000
##   UN =~                                               
##     un01              1.000                           
##     un02              1.212    0.064   18.933    0.000
##     un03              0.811    0.068   11.917    0.000
##     un04              1.022    0.062   16.529    0.000
##     un05              1.140    0.062   18.327    0.000
##     un06              0.780    0.072   10.824    0.000
##     un07              1.035    0.067   15.351    0.000
##     un08              1.118    0.066   16.974    0.000
##     un09              1.085    0.070   15.413    0.000
##     un10              1.046    0.066   15.904    0.000
##     un11              1.188    0.068   17.430    0.000
##     un12              1.061    0.064   16.562    0.000
##   DT =~                                               
##     PR                1.000                           
##     CO                0.756    0.061   12.410    0.000
##     UT                0.817    0.060   13.611    0.000
##     FA                0.774    0.065   11.940    0.000
##     DE                0.781    0.067   11.715    0.000
##     UN                0.415    0.054    7.755    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .pr01              0.377    0.029   13.111    0.000
##    .pr02              0.616    0.041   14.878    0.000
##    .pr05              0.964    0.065   14.939    0.000
##    .pr06              0.761    0.051   14.909    0.000
##    .pr07              0.577    0.042   13.851    0.000
##    .pr08              0.392    0.028   13.920    0.000
##    .pr09              0.596    0.040   14.874    0.000
##    .pr10              0.807    0.053   15.197    0.000
##    .co01              0.528    0.040   13.134    0.000
##    .co02              0.578    0.042   13.840    0.000
##    .co03              0.774    0.052   14.983    0.000
##    .co04              1.037    0.067   15.438    0.000
##    .co05              0.485    0.039   12.379    0.000
##    .co06              0.759    0.053   14.396    0.000
##    .co08              0.959    0.062   15.464    0.000
##    .co09              0.540    0.041   13.307    0.000
##    .co10              0.760    0.052   14.482    0.000
##    .ut01              0.442    0.033   13.480    0.000
##    .ut02              0.336    0.027   12.276    0.000
##    .ut03              0.927    0.061   15.252    0.000
##    .ut04              0.959    0.063   15.318    0.000
##    .ut05              0.835    0.057   14.611    0.000
##    .ut06              0.528    0.038   13.820    0.000
##    .ut07              0.863    0.058   15.013    0.000
##    .ut08              0.672    0.046   14.773    0.000
##    .ut09              0.775    0.053   14.590    0.000
##    .ut11              1.106    0.073   15.177    0.000
##    .ut12              0.690    0.048   14.370    0.000
##    .fa01              0.364    0.035   10.321    0.000
##    .fa02              1.164    0.076   15.308    0.000
##    .fa04              1.041    0.067   15.452    0.000
##    .fa05              0.347    0.035    9.969    0.000
##    .fa06              0.759    0.052   14.556    0.000
##    .fa08              1.102    0.072   15.365    0.000
##    .fa09              1.081    0.072   15.106    0.000
##    .fa10              0.914    0.063   14.471    0.000
##    .de01              0.820    0.055   14.910    0.000
##    .de02              0.680    0.049   14.009    0.000
##    .de03              0.905    0.062   14.710    0.000
##    .de05              0.971    0.065   15.041    0.000
##    .de06              0.908    0.062   14.556    0.000
##    .de07              0.627    0.043   14.679    0.000
##    .de08              0.579    0.041   14.074    0.000
##    .de09              1.296    0.083   15.544    0.000
##    .de10              0.690    0.050   13.890    0.000
##    .un01              0.497    0.035   14.368    0.000
##    .un02              0.399    0.030   13.230    0.000
##    .un03              0.966    0.063   15.272    0.000
##    .un04              0.541    0.038   14.423    0.000
##    .un05              0.423    0.031   13.641    0.000
##    .un06              1.148    0.075   15.374    0.000
##    .un07              0.731    0.050   14.739    0.000
##    .un08              0.581    0.041   14.271    0.000
##    .un09              0.792    0.054   14.725    0.000
##    .un10              0.657    0.045   14.604    0.000
##    .un11              0.584    0.041   14.092    0.000
##    .un12              0.578    0.040   14.412    0.000
##    .PR                0.045    0.017    2.670    0.008
##    .CO                0.317    0.039    8.191    0.000
##    .UT                0.294    0.034    8.763    0.000
##    .FA                0.516    0.052    9.864    0.000
##    .DE                0.044    0.013    3.465    0.001
##    .UN                0.513    0.055    9.332    0.000
##     DT                0.549    0.058    9.485    0.000

tibble(
  `Model 3` = c(
    "Chi-Squared",
    "DF",
    "p",
    "GFI",
    "AGFI",
    "CFI",
    "TLI",
    "SRMR",
    "RMSEA"
  ),
  Value = round(fitmeasures(
    model3,
    c(
      "chisq",
      "df",
      "pvalue",
      "gfi",
      "agfi",
      "cfi",
      "tli",
      "srmr",
      "rmsea"
    )
  ), 4)
) %>% kable()

Model 3	Value
Chi-Squared	5261.5090
DF	1533.0000
p	0.0000
GFI	0.6731
AGFI	0.6476
CFI	0.7446
TLI	0.7341
SRMR	0.1013
RMSEA	0.0701

Standardized solution:

smodel3 <- standardizedsolution(model3)

Loadings:

smodel3 %>%
  filter(op == "=~") %>%
  kable(
    col.names = c(
      "Factor",
      "",
      "Item",
      "Loading",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )

Factor		Item	Loading	SE	z	p	CI lower bound	CI upper bound
PR	=~	pr01	0.782	0.020	38.417	0	0.742	0.822
PR	=~	pr02	0.587	0.032	18.400	0	0.524	0.649
PR	=~	pr05	0.573	0.033	17.559	0	0.509	0.637
PR	=~	pr06	0.580	0.032	17.974	0	0.517	0.643
PR	=~	pr07	0.730	0.024	30.840	0	0.684	0.776
PR	=~	pr08	0.724	0.024	30.081	0	0.677	0.771
PR	=~	pr09	0.588	0.032	18.451	0	0.525	0.650
PR	=~	pr10	0.498	0.036	13.787	0	0.427	0.569
CO	=~	co01	0.738	0.024	30.452	0	0.690	0.785
CO	=~	co02	0.683	0.028	24.821	0	0.629	0.737
CO	=~	co03	0.508	0.037	13.911	0	0.436	0.580
CO	=~	co04	0.347	0.043	8.149	0	0.264	0.430
CO	=~	co05	0.779	0.022	35.840	0	0.736	0.821
CO	=~	co06	0.618	0.031	19.865	0	0.557	0.679
CO	=~	co08	0.333	0.043	7.740	0	0.249	0.417
CO	=~	co09	0.726	0.025	29.132	0	0.677	0.775
CO	=~	co10	0.605	0.032	19.048	0	0.543	0.668
UT	=~	ut01	0.774	0.021	37.612	0	0.734	0.814
UT	=~	ut02	0.834	0.017	50.496	0	0.802	0.867
UT	=~	ut03	0.493	0.036	13.613	0	0.422	0.564
UT	=~	ut04	0.466	0.037	12.469	0	0.393	0.539
UT	=~	ut05	0.654	0.028	23.325	0	0.599	0.709
UT	=~	ut06	0.748	0.022	33.579	0	0.704	0.792
UT	=~	ut07	0.569	0.033	17.471	0	0.506	0.633
UT	=~	ut08	0.625	0.030	21.049	0	0.566	0.683
UT	=~	ut09	0.657	0.028	23.629	0	0.603	0.712
UT	=~	ut11	0.520	0.035	14.879	0	0.452	0.589
UT	=~	ut12	0.690	0.026	26.601	0	0.639	0.740
FA	=~	fa01	0.836	0.019	44.351	0	0.799	0.873
FA	=~	fa02	0.403	0.041	9.877	0	0.323	0.483
FA	=~	fa04	0.337	0.043	7.848	0	0.253	0.421
FA	=~	fa05	0.845	0.018	45.872	0	0.809	0.881
FA	=~	fa06	0.589	0.033	18.019	0	0.525	0.653
FA	=~	fa08	0.379	0.042	9.114	0	0.298	0.461
FA	=~	fa09	0.471	0.038	12.352	0	0.396	0.546
FA	=~	fa10	0.602	0.032	18.816	0	0.540	0.665
DE	=~	de01	0.562	0.033	16.829	0	0.497	0.627
DE	=~	de02	0.699	0.026	27.038	0	0.649	0.750
DE	=~	de03	0.603	0.031	19.291	0	0.542	0.665
DE	=~	de05	0.529	0.035	15.120	0	0.460	0.598
DE	=~	de06	0.630	0.030	21.094	0	0.571	0.688
DE	=~	de07	0.609	0.031	19.655	0	0.548	0.670
DE	=~	de08	0.692	0.026	26.365	0	0.641	0.744
DE	=~	de09	0.310	0.043	7.189	0	0.226	0.395
DE	=~	de10	0.711	0.025	28.245	0	0.661	0.760
UN	=~	un01	0.742	0.022	33.751	0	0.699	0.785
UN	=~	un02	0.831	0.016	52.499	0	0.800	0.862
UN	=~	un03	0.541	0.033	16.192	0	0.476	0.606
UN	=~	un04	0.735	0.022	32.773	0	0.691	0.779
UN	=~	un05	0.807	0.018	46.033	0	0.773	0.842
UN	=~	un06	0.493	0.036	13.855	0	0.424	0.563
UN	=~	un07	0.686	0.025	26.955	0	0.637	0.736
UN	=~	un08	0.753	0.021	35.453	0	0.711	0.795
UN	=~	un09	0.689	0.025	27.225	0	0.639	0.739
UN	=~	un10	0.709	0.024	29.486	0	0.662	0.756
UN	=~	un11	0.771	0.020	38.556	0	0.732	0.810
UN	=~	un12	0.736	0.022	32.962	0	0.692	0.780
DT	=~	PR	0.961	0.014	66.441	0	0.933	0.990
DT	=~	CO	0.705	0.029	24.250	0	0.648	0.762
DT	=~	UT	0.745	0.025	29.284	0	0.695	0.795
DT	=~	FA	0.624	0.034	18.548	0	0.558	0.690
DT	=~	DE	0.941	0.016	60.324	0	0.910	0.971
DT	=~	UN	0.395	0.042	9.399	0	0.313	0.477

Covariances:

smodel3 %>%
  filter(op == "~~" & lhs != rhs) %>%
  kable(
    col.names = c(
      "Factor",
      "",
      "Factor",
      "Covariance",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )

Factor		Factor	Covariance	SE	z	p	CI lower bound	CI upper bound

Residuals:

smodel3 %>%
  filter(op == "~~" & lhs == rhs) %>%
  select(-(2:3)) %>%
  kable(
    col.names = c(
      "Item",
      "Residual",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )

Item	Residual	SE	z	p	CI lower bound	CI upper bound
pr01	0.388	0.032	12.200	0.000	0.326	0.451
pr02	0.656	0.037	17.513	0.000	0.582	0.729
pr05	0.672	0.037	17.986	0.000	0.599	0.745
pr06	0.664	0.037	17.747	0.000	0.591	0.737
pr07	0.467	0.035	13.509	0.000	0.399	0.535
pr08	0.476	0.035	13.666	0.000	0.408	0.544
pr09	0.655	0.037	17.485	0.000	0.581	0.728
pr10	0.752	0.036	20.870	0.000	0.681	0.822
co01	0.456	0.036	12.746	0.000	0.386	0.526
co02	0.534	0.038	14.218	0.000	0.460	0.608
co03	0.742	0.037	20.004	0.000	0.669	0.815
co04	0.880	0.030	29.771	0.000	0.822	0.938
co05	0.394	0.034	11.637	0.000	0.327	0.460
co06	0.618	0.038	16.073	0.000	0.543	0.693
co08	0.889	0.029	31.064	0.000	0.833	0.945
co09	0.473	0.036	13.054	0.000	0.402	0.544
co10	0.634	0.038	16.465	0.000	0.558	0.709
ut01	0.401	0.032	12.597	0.000	0.339	0.464
ut02	0.304	0.028	11.030	0.000	0.250	0.358
ut03	0.757	0.036	21.220	0.000	0.687	0.827
ut04	0.783	0.035	22.504	0.000	0.715	0.851
ut05	0.573	0.037	15.622	0.000	0.501	0.644
ut06	0.441	0.033	13.228	0.000	0.375	0.506
ut07	0.676	0.037	18.209	0.000	0.603	0.749
ut08	0.610	0.037	16.452	0.000	0.537	0.683
ut09	0.568	0.037	15.523	0.000	0.496	0.640
ut11	0.729	0.036	20.046	0.000	0.658	0.801
ut12	0.525	0.036	14.674	0.000	0.454	0.595
fa01	0.301	0.032	9.535	0.000	0.239	0.363
fa02	0.838	0.033	25.501	0.000	0.773	0.902
fa04	0.886	0.029	30.644	0.000	0.830	0.943
fa05	0.287	0.031	9.210	0.000	0.226	0.347
fa06	0.653	0.039	16.940	0.000	0.577	0.728
fa08	0.856	0.032	27.148	0.000	0.794	0.918
fa09	0.778	0.036	21.634	0.000	0.707	0.848
fa10	0.637	0.039	16.511	0.000	0.561	0.713
de01	0.684	0.038	18.225	0.000	0.611	0.758
de02	0.511	0.036	14.134	0.000	0.440	0.582
de03	0.636	0.038	16.847	0.000	0.562	0.710
de05	0.720	0.037	19.459	0.000	0.648	0.793
de06	0.603	0.038	16.045	0.000	0.530	0.677
de07	0.629	0.038	16.673	0.000	0.555	0.703
de08	0.521	0.036	14.311	0.000	0.449	0.592
de09	0.904	0.027	33.806	0.000	0.851	0.956
de10	0.495	0.036	13.835	0.000	0.425	0.565
un01	0.450	0.033	13.803	0.000	0.386	0.514
un02	0.309	0.026	11.739	0.000	0.257	0.361
un03	0.707	0.036	19.566	0.000	0.636	0.778
un04	0.460	0.033	13.962	0.000	0.395	0.525
un05	0.348	0.028	12.311	0.000	0.293	0.404
un06	0.757	0.035	21.530	0.000	0.688	0.825
un07	0.529	0.035	15.127	0.000	0.460	0.597
un08	0.433	0.032	13.546	0.000	0.370	0.496
un09	0.525	0.035	15.062	0.000	0.457	0.594
un10	0.497	0.034	14.566	0.000	0.430	0.564
un11	0.405	0.031	13.128	0.000	0.345	0.466
un12	0.458	0.033	13.930	0.000	0.394	0.523
PR	0.076	0.028	2.729	0.006	0.021	0.130
CO	0.503	0.041	12.262	0.000	0.422	0.583
UT	0.445	0.038	11.731	0.000	0.370	0.519
FA	0.611	0.042	14.542	0.000	0.528	0.693
DE	0.115	0.029	3.932	0.000	0.058	0.173
UN	0.844	0.033	25.430	0.000	0.779	0.909
DT	1.000	0.000	NA	NA	1.000	1.000

semPaths(model3,
         what = "std",
         whatLabels = "est",
         style = "lisrel",
         residScale = 10,
         theme = "colorblind",
         rotation = 1,
         layout = "tree",
         cardinal = "lat cov",
         curvePivot = TRUE,
         sizeMan = 3,
         sizeLat = 7)

Predicted values

lavPredict(model3, taia %>% select(all_of(taia_items_2))) %>% 
  as_tibble() %>% 
  mutate(id = taia$id) %>% 
  full_join(
    taia %>% 
      select(id, all_of(taia_items_2)) %>% 
      pivot_longer(cols = all_of(taia_items_2)) %>% 
      mutate(name = toupper(str_replace_all(name, "[:digit:]{2}", ""))) %>% 
      group_by(id, name) %>% 
      summarise(score = sum(value)) %>% 
      pivot_wider(id_cols = id, names_from = name, names_prefix = "s_", values_from = score) %>% 
      relocate(after = c(id, s_PR, s_CO, s_UT, s_FA, s_DE, s_UN)) %>% 
      mutate(s_DT = s_PR + s_CO + s_UT + s_FA + s_DE + s_UN)
  ) -> predicted_and_direct_sums

## `summarise()` regrouping output by 'id' (override with `.groups` argument)

## Joining, by = "id"

ggcorrplot(cor(predicted_and_direct_sums %>% select(-id))[-(1:7), -(8:14)],
           lab = TRUE,
           colors = c("indianred1", "white", "royalblue1"))

Graph shows high correlation between fitted values and direct sums, so we can work with the later.

Model modification

ggcorrplot(cor(predicted_and_direct_sums %>% 
                 select(-id, -PR, -CO, -UT, -FA, -DE, -UN, -DT)),
           type = "lower", lab = TRUE,
           colors = c("indianred1", "white", "royalblue1"))

modificationindices(model3) %>% arrange(desc(mi)) -> modif3

Measures:

modif3 %>% 
  filter(op == "=~") %>% 
  arrange(lhs) %>% 
  kable(digits = 2,
        col.names = c("Factor", "", "Item", "Modification Index", "epc", "sepc.lv", "sepc.all", "sepc.nox"))

Factor		Item	Modification Index	epc	sepc.lv	sepc.all	sepc.nox
CO	=~	fa06	96.43	0.62	0.49	0.46	0.46
CO	=~	fa04	61.19	0.56	0.45	0.41	0.41
CO	=~	fa02	46.88	-0.52	-0.41	-0.35	-0.35
CO	=~	fa08	44.78	-0.50	-0.39	-0.35	-0.35
CO	=~	ut03	34.67	-0.43	-0.34	-0.31	-0.31
CO	=~	fa09	33.63	-0.43	-0.34	-0.29	-0.29
CO	=~	ut11	26.27	0.41	0.32	0.26	0.26
CO	=~	un07	22.01	0.26	0.21	0.18	0.18
CO	=~	ut02	20.42	-0.22	-0.18	-0.17	-0.17
CO	=~	ut01	18.65	-0.23	-0.18	-0.18	-0.18
CO	=~	un10	9.29	0.16	0.13	0.11	0.11
CO	=~	de05	7.82	-0.25	-0.20	-0.17	-0.17
CO	=~	ut05	7.50	0.19	0.15	0.13	0.13
CO	=~	ut08	7.42	0.17	0.14	0.13	0.13
CO	=~	de09	5.24	-0.23	-0.19	-0.16	-0.16
CO	=~	fa10	4.90	-0.15	-0.12	-0.10	-0.10
CO	=~	pr09	4.84	-0.16	-0.13	-0.13	-0.13
CO	=~	fa05	4.82	0.11	0.09	0.08	0.08
CO	=~	de02	3.94	0.16	0.12	0.11	0.11
CO	=~	un06	3.94	-0.14	-0.11	-0.09	-0.09
CO	=~	ut04	3.52	-0.14	-0.11	-0.10	-0.10
CO	=~	un11	3.15	-0.09	-0.07	-0.06	-0.06
CO	=~	ut07	3.14	0.13	0.10	0.09	0.09
CO	=~	ut12	3.12	0.11	0.09	0.08	0.08
CO	=~	de03	2.76	0.15	0.12	0.10	0.10
CO	=~	pr05	2.41	0.14	0.11	0.09	0.09
CO	=~	de01	2.24	0.13	0.10	0.09	0.09
CO	=~	ut09	1.95	0.10	0.08	0.06	0.06
CO	=~	un12	1.92	-0.07	-0.06	-0.05	-0.05
CO	=~	pr02	1.77	0.10	0.08	0.08	0.08
CO	=~	pr07	1.73	0.10	0.08	0.07	0.07
CO	=~	un01	1.60	-0.06	-0.05	-0.04	-0.04
CO	=~	un09	1.60	0.07	0.06	0.05	0.05
CO	=~	de08	1.43	-0.09	-0.07	-0.07	-0.07
CO	=~	un08	1.19	-0.06	-0.04	-0.04	-0.04
CO	=~	un05	0.99	-0.04	-0.03	-0.03	-0.03
CO	=~	de10	0.79	0.07	0.06	0.05	0.05
CO	=~	pr06	0.54	-0.06	-0.05	-0.04	-0.04
CO	=~	fa01	0.39	-0.03	-0.03	-0.02	-0.02
CO	=~	un04	0.18	0.02	0.02	0.01	0.01
CO	=~	de07	0.11	-0.02	-0.02	-0.02	-0.02
CO	=~	un02	0.10	-0.01	-0.01	-0.01	-0.01
CO	=~	pr01	0.09	0.02	0.02	0.02	0.02
CO	=~	pr08	0.08	-0.02	-0.01	-0.01	-0.01
CO	=~	un03	0.01	-0.01	0.00	0.00	0.00
CO	=~	ut06	0.01	0.01	0.00	0.00	0.00
CO	=~	de06	0.00	-0.01	0.00	0.00	0.00
CO	=~	pr10	0.00	0.00	0.00	0.00	0.00
DE	=~	fa06	131.37	1.07	0.66	0.61	0.61
DE	=~	ut11	77.11	1.13	0.70	0.56	0.56
DE	=~	co03	66.49	0.84	0.52	0.51	0.51
DE	=~	fa02	63.44	-0.89	-0.55	-0.46	-0.46
DE	=~	co08	44.14	-0.75	-0.46	-0.44	-0.44
DE	=~	co04	37.47	0.72	0.44	0.41	0.41
DE	=~	ut01	37.12	-0.54	-0.33	-0.31	-0.31
DE	=~	fa08	34.39	-0.64	-0.39	-0.34	-0.34
DE	=~	fa09	33.97	-0.63	-0.39	-0.33	-0.33
DE	=~	pr05	30.04	1.61	0.99	0.83	0.83
DE	=~	co09	22.90	-0.45	-0.28	-0.26	-0.26
DE	=~	fa04	22.02	0.49	0.30	0.28	0.28
DE	=~	ut02	18.30	-0.35	-0.22	-0.20	-0.20
DE	=~	un06	15.22	-0.36	-0.22	-0.18	-0.18
DE	=~	ut12	12.92	0.38	0.23	0.20	0.20
DE	=~	co01	11.78	0.32	0.20	0.18	0.18
DE	=~	co05	10.33	-0.30	-0.19	-0.17	-0.17
DE	=~	pr01	9.14	-0.65	-0.40	-0.41	-0.41
DE	=~	fa10	7.55	-0.28	-0.17	-0.14	-0.14
DE	=~	ut08	6.22	0.25	0.16	0.15	0.15
DE	=~	ut03	5.98	-0.29	-0.18	-0.16	-0.16
DE	=~	un07	5.76	0.18	0.11	0.09	0.09
DE	=~	pr06	4.51	-0.55	-0.34	-0.32	-0.32
DE	=~	fa05	4.41	0.17	0.10	0.09	0.09
DE	=~	un11	3.96	-0.14	-0.08	-0.07	-0.07
DE	=~	un09	3.57	0.15	0.09	0.07	0.07
DE	=~	pr08	3.44	-0.38	-0.23	-0.26	-0.26
DE	=~	un12	1.25	0.07	0.05	0.04	0.04
DE	=~	pr07	1.22	0.28	0.17	0.15	0.15
DE	=~	ut09	1.11	0.12	0.07	0.06	0.06
DE	=~	co02	1.01	0.09	0.06	0.06	0.06
DE	=~	un04	1.00	-0.06	-0.04	-0.04	-0.04
DE	=~	pr10	0.91	-0.25	-0.15	-0.15	-0.15
DE	=~	co06	0.90	-0.10	-0.06	-0.06	-0.06
DE	=~	ut07	0.75	0.10	0.06	0.05	0.05
DE	=~	un05	0.73	0.05	0.03	0.03	0.03
DE	=~	un02	0.66	-0.05	-0.03	-0.03	-0.03
DE	=~	ut06	0.60	0.07	0.04	0.04	0.04
DE	=~	un01	0.29	0.03	0.02	0.02	0.02
DE	=~	ut05	0.29	0.06	0.04	0.03	0.03
DE	=~	pr02	0.16	0.10	0.06	0.06	0.06
DE	=~	un03	0.12	0.03	0.02	0.02	0.02
DE	=~	fa01	0.11	-0.03	-0.02	-0.01	-0.01
DE	=~	ut04	0.06	0.03	0.02	0.02	0.02
DE	=~	pr09	0.01	0.02	0.01	0.02	0.02
DE	=~	co10	0.01	0.01	0.01	0.01	0.01
DE	=~	un08	0.00	0.00	0.00	0.00	0.00
DE	=~	un10	0.00	0.00	0.00	0.00	0.00
DT	=~	fa06	139.75	0.95	0.71	0.65	0.65
DT	=~	co03	79.85	0.82	0.61	0.59	0.59
DT	=~	fa02	75.65	-0.84	-0.62	-0.53	-0.53
DT	=~	ut11	71.86	0.98	0.73	0.59	0.59
DT	=~	co04	62.54	0.82	0.61	0.56	0.56
DT	=~	co08	60.53	-0.78	-0.57	-0.55	-0.55
DT	=~	fa08	44.54	-0.62	-0.46	-0.41	-0.41
DT	=~	fa09	38.52	-0.58	-0.43	-0.37	-0.37
DT	=~	ut01	37.14	-0.48	-0.36	-0.34	-0.34
DT	=~	co09	28.42	-0.44	-0.33	-0.31	-0.31
DT	=~	de05	23.82	1.77	1.31	1.13	1.13
DT	=~	pr05	23.22	2.61	1.93	1.61	1.61
DT	=~	ut02	16.84	-0.30	-0.22	-0.21	-0.21
DT	=~	un06	15.87	-0.30	-0.23	-0.18	-0.18
DT	=~	fa04	15.80	0.36	0.27	0.25	0.25
DT	=~	co05	13.51	-0.31	-0.23	-0.20	-0.20
DT	=~	ut12	12.72	0.34	0.25	0.22	0.22
DT	=~	co01	10.19	0.27	0.20	0.18	0.18
DT	=~	ut08	8.64	0.27	0.20	0.19	0.19
DT	=~	de10	8.29	-0.99	-0.73	-0.62	-0.62
DT	=~	de07	7.14	0.81	0.60	0.60	0.60
DT	=~	ut03	7.03	-0.28	-0.21	-0.19	-0.19
DT	=~	de09	6.76	1.04	0.77	0.64	0.64
DT	=~	fa10	6.35	-0.22	-0.17	-0.14	-0.14
DT	=~	un11	4.86	-0.13	-0.09	-0.08	-0.08
DT	=~	de03	4.67	-0.78	-0.58	-0.49	-0.49
DT	=~	un07	3.71	0.12	0.09	0.08	0.08
DT	=~	fa05	2.62	0.11	0.08	0.08	0.08
DT	=~	de08	1.98	-0.44	-0.32	-0.31	-0.31
DT	=~	pr01	1.97	-0.58	-0.43	-0.44	-0.44
DT	=~	co02	1.83	0.11	0.08	0.08	0.08
DT	=~	un01	1.77	0.07	0.05	0.05	0.05
DT	=~	co06	1.66	-0.12	-0.09	-0.08	-0.08
DT	=~	ut07	1.64	0.13	0.10	0.09	0.09
DT	=~	pr08	1.52	-0.48	-0.35	-0.39	-0.39
DT	=~	un12	1.50	0.07	0.05	0.05	0.05
DT	=~	ut09	1.41	0.12	0.09	0.07	0.07
DT	=~	un09	1.31	0.07	0.06	0.04	0.04
DT	=~	un02	1.26	-0.05	-0.04	-0.04	-0.04
DT	=~	de02	1.26	-0.38	-0.28	-0.24	-0.24
DT	=~	pr10	1.22	-0.53	-0.39	-0.38	-0.38
DT	=~	pr07	1.16	0.51	0.38	0.34	0.34
DT	=~	un05	1.10	0.05	0.04	0.03	0.03
DT	=~	pr09	0.89	-0.40	-0.30	-0.31	-0.31
DT	=~	ut06	0.84	0.08	0.06	0.05	0.05
DT	=~	pr06	0.47	-0.33	-0.25	-0.23	-0.23
DT	=~	un04	0.41	-0.03	-0.03	-0.02	-0.02
DT	=~	ut05	0.20	0.05	0.03	0.03	0.03
DT	=~	ut04	0.20	-0.05	-0.04	-0.03	-0.03
DT	=~	co10	0.17	0.04	0.03	0.03	0.03
DT	=~	de01	0.12	0.12	0.09	0.08	0.08
DT	=~	un03	0.09	0.02	0.02	0.01	0.01
DT	=~	de06	0.09	0.11	0.08	0.07	0.07
DT	=~	un10	0.07	-0.02	-0.01	-0.01	-0.01
DT	=~	un08	0.03	0.01	0.01	0.01	0.01
DT	=~	pr02	0.00	0.02	0.01	0.01	0.01
DT	=~	fa01	0.00	0.00	0.00	0.00	0.00
FA	=~	co08	21.44	-0.28	-0.25	-0.24	-0.24
FA	=~	co03	19.45	0.24	0.22	0.22	0.22
FA	=~	co01	17.95	0.20	0.19	0.17	0.17
FA	=~	ut11	15.85	0.26	0.24	0.19	0.19
FA	=~	co04	15.75	0.25	0.23	0.21	0.21
FA	=~	un06	11.90	-0.20	-0.19	-0.15	-0.15
FA	=~	pr10	10.43	-0.21	-0.19	-0.18	-0.18
FA	=~	de10	9.04	0.18	0.17	0.14	0.14
FA	=~	co06	8.52	-0.16	-0.15	-0.13	-0.13
FA	=~	un03	8.16	-0.15	-0.14	-0.12	-0.12
FA	=~	ut01	7.87	-0.12	-0.11	-0.11	-0.11
FA	=~	co09	7.36	-0.13	-0.12	-0.11	-0.11
FA	=~	co02	6.78	0.13	0.12	0.11	0.11
FA	=~	de06	6.63	0.18	0.16	0.13	0.13
FA	=~	pr06	5.63	-0.15	-0.14	-0.13	-0.13
FA	=~	un01	4.88	0.09	0.08	0.08	0.08
FA	=~	un11	4.13	-0.09	-0.08	-0.07	-0.07
FA	=~	ut02	2.84	-0.07	-0.06	-0.06	-0.06
FA	=~	pr05	2.81	0.12	0.11	0.09	0.09
FA	=~	co10	2.63	-0.09	-0.08	-0.07	-0.07
FA	=~	un05	2.32	0.06	0.05	0.05	0.05
FA	=~	de05	2.25	-0.10	-0.10	-0.08	-0.08
FA	=~	de03	2.05	0.10	0.09	0.08	0.08
FA	=~	de07	1.72	-0.07	-0.07	-0.07	-0.07
FA	=~	pr08	1.67	-0.06	-0.06	-0.06	-0.06
FA	=~	pr02	1.53	-0.07	-0.06	-0.07	-0.07
FA	=~	co05	1.34	-0.05	-0.05	-0.05	-0.05
FA	=~	pr07	1.24	0.06	0.06	0.05	0.05
FA	=~	ut03	1.17	0.06	0.06	0.05	0.05
FA	=~	un04	1.15	-0.04	-0.04	-0.04	-0.04
FA	=~	un10	0.80	-0.04	-0.04	-0.03	-0.03
FA	=~	ut04	0.64	0.05	0.04	0.04	0.04
FA	=~	ut08	0.57	0.04	0.04	0.03	0.03
FA	=~	un07	0.56	0.04	0.03	0.03	0.03
FA	=~	ut07	0.54	0.04	0.04	0.03	0.03
FA	=~	un08	0.53	0.03	0.03	0.02	0.02
FA	=~	pr09	0.45	-0.04	-0.03	-0.04	-0.04
FA	=~	de02	0.42	0.04	0.04	0.03	0.03
FA	=~	un02	0.35	-0.02	-0.02	-0.02	-0.02
FA	=~	ut06	0.33	0.03	0.03	0.02	0.02
FA	=~	ut09	0.24	-0.03	-0.03	-0.02	-0.02
FA	=~	ut05	0.09	0.02	0.02	0.01	0.01
FA	=~	de09	0.09	-0.02	-0.02	-0.02	-0.02
FA	=~	un12	0.06	-0.01	-0.01	-0.01	-0.01
FA	=~	de08	0.03	0.01	0.01	0.01	0.01
FA	=~	de01	0.02	0.01	0.01	0.01	0.01
FA	=~	ut12	0.00	0.00	0.00	0.00	0.00
FA	=~	pr01	0.00	0.00	0.00	0.00	0.00
FA	=~	un09	0.00	0.00	0.00	0.00	0.00
PR	=~	fa06	134.64	0.87	0.67	0.62	0.62
PR	=~	co03	77.18	0.74	0.57	0.56	0.56
PR	=~	fa02	73.45	-0.77	-0.59	-0.50	-0.50
PR	=~	ut11	62.90	0.83	0.64	0.52	0.52
PR	=~	co04	62.73	0.75	0.58	0.53	0.53
PR	=~	co08	59.54	-0.70	-0.54	-0.52	-0.52
PR	=~	fa08	41.01	-0.56	-0.43	-0.38	-0.38
PR	=~	fa09	34.42	-0.51	-0.40	-0.34	-0.34
PR	=~	ut01	34.08	-0.42	-0.32	-0.31	-0.31
PR	=~	co09	25.92	-0.39	-0.30	-0.28	-0.28
PR	=~	de05	23.16	1.12	0.87	0.74	0.74
PR	=~	un06	15.26	-0.28	-0.22	-0.18	-0.18
PR	=~	ut12	12.98	0.31	0.24	0.21	0.21
PR	=~	ut02	12.92	-0.24	-0.18	-0.18	-0.18
PR	=~	co05	11.98	-0.26	-0.20	-0.18	-0.18
PR	=~	fa04	11.62	0.29	0.22	0.20	0.20
PR	=~	de10	11.52	-0.74	-0.57	-0.49	-0.49
PR	=~	ut08	9.63	0.26	0.20	0.19	0.19
PR	=~	de09	7.51	0.71	0.54	0.45	0.45
PR	=~	co01	7.27	0.21	0.16	0.15	0.15
PR	=~	de03	6.68	-0.60	-0.46	-0.39	-0.39
PR	=~	fa10	6.31	-0.21	-0.16	-0.13	-0.13
PR	=~	ut03	4.84	-0.21	-0.16	-0.15	-0.15
PR	=~	un11	4.50	-0.12	-0.09	-0.07	-0.07
PR	=~	de07	4.43	0.41	0.31	0.31	0.31
PR	=~	un07	2.45	0.09	0.07	0.06	0.06
PR	=~	co02	2.40	0.12	0.09	0.09	0.09
PR	=~	un01	2.35	0.08	0.06	0.06	0.06
PR	=~	de08	2.21	-0.29	-0.23	-0.21	-0.21
PR	=~	ut07	1.97	0.13	0.10	0.09	0.09
PR	=~	co06	1.82	-0.12	-0.09	-0.08	-0.08
PR	=~	un12	1.79	0.07	0.06	0.05	0.05
PR	=~	un02	1.60	-0.06	-0.05	-0.04	-0.04
PR	=~	ut09	1.59	0.11	0.09	0.08	0.08
PR	=~	un05	1.38	0.06	0.04	0.04	0.04
PR	=~	fa05	1.25	0.07	0.06	0.05	0.05
PR	=~	ut06	1.20	0.08	0.07	0.06	0.06
PR	=~	un09	0.77	0.05	0.04	0.03	0.03
PR	=~	ut04	0.56	-0.07	-0.06	-0.05	-0.05
PR	=~	co10	0.41	0.05	0.04	0.04	0.04
PR	=~	de02	0.28	-0.11	-0.09	-0.08	-0.08
PR	=~	un04	0.20	-0.02	-0.02	-0.02	-0.02
PR	=~	un10	0.09	-0.02	-0.01	-0.01	-0.01
PR	=~	un08	0.08	0.02	0.01	0.01	0.01
PR	=~	un03	0.07	0.02	0.01	0.01	0.01
PR	=~	fa01	0.01	0.01	0.01	0.00	0.00
PR	=~	de06	0.00	0.01	0.01	0.01	0.01
PR	=~	de01	0.00	0.01	0.01	0.00	0.00
PR	=~	ut05	0.00	0.00	0.00	0.00	0.00
UN	=~	fa02	56.03	-0.51	-0.40	-0.34	-0.34
UN	=~	fa08	47.24	-0.45	-0.35	-0.31	-0.31
UN	=~	fa09	36.97	-0.40	-0.31	-0.26	-0.26
UN	=~	fa06	28.05	0.30	0.23	0.21	0.21
UN	=~	co10	16.84	0.23	0.18	0.17	0.17
UN	=~	co08	14.61	-0.24	-0.18	-0.18	-0.18
UN	=~	ut11	11.47	0.23	0.18	0.14	0.14
UN	=~	ut03	10.84	-0.20	-0.16	-0.14	-0.14
UN	=~	de07	9.76	0.17	0.13	0.13	0.13
UN	=~	co04	8.55	0.19	0.15	0.14	0.14
UN	=~	de10	8.50	-0.17	-0.13	-0.11	-0.11
UN	=~	co05	7.66	-0.13	-0.11	-0.09	-0.09
UN	=~	co09	7.08	-0.13	-0.10	-0.10	-0.10
UN	=~	pr02	6.66	0.14	0.11	0.11	0.11
UN	=~	fa10	5.93	-0.15	-0.12	-0.10	-0.10
UN	=~	pr07	4.32	-0.11	-0.09	-0.08	-0.08
UN	=~	ut12	4.00	0.11	0.09	0.07	0.07
UN	=~	fa04	3.95	0.13	0.10	0.09	0.09
UN	=~	co03	3.50	0.11	0.08	0.08	0.08
UN	=~	de06	1.89	0.09	0.07	0.06	0.06
UN	=~	de08	1.65	0.07	0.05	0.05	0.05
UN	=~	de01	1.43	0.07	0.06	0.05	0.05
UN	=~	pr06	1.41	0.07	0.05	0.05	0.05
UN	=~	de05	1.18	0.07	0.06	0.05	0.05
UN	=~	co02	1.13	0.05	0.04	0.04	0.04
UN	=~	ut06	0.81	-0.04	-0.03	-0.03	-0.03
UN	=~	fa01	0.67	0.04	0.03	0.03	0.03
UN	=~	pr10	0.65	0.05	0.04	0.04	0.04
UN	=~	fa05	0.56	0.03	0.03	0.02	0.02
UN	=~	pr05	0.55	0.05	0.04	0.03	0.03
UN	=~	co06	0.53	0.04	0.03	0.03	0.03
UN	=~	pr08	0.50	-0.03	-0.02	-0.03	-0.03
UN	=~	de02	0.50	-0.04	-0.03	-0.03	-0.03
UN	=~	ut05	0.40	0.04	0.03	0.02	0.02
UN	=~	ut04	0.37	-0.04	-0.03	-0.03	-0.03
UN	=~	de09	0.37	-0.05	-0.04	-0.03	-0.03
UN	=~	ut01	0.33	-0.03	-0.02	-0.02	-0.02
UN	=~	de03	0.27	-0.03	-0.03	-0.02	-0.02
UN	=~	ut07	0.27	-0.03	-0.02	-0.02	-0.02
UN	=~	pr01	0.11	0.01	0.01	0.01	0.01
UN	=~	co01	0.10	0.02	0.01	0.01	0.01
UN	=~	ut02	0.07	-0.01	-0.01	-0.01	-0.01
UN	=~	pr09	0.02	-0.01	-0.01	-0.01	-0.01
UN	=~	ut08	0.01	0.01	0.00	0.00	0.00
UN	=~	ut09	0.01	0.00	0.00	0.00	0.00
UT	=~	co04	112.78	0.78	0.63	0.58	0.58
UT	=~	co03	71.66	0.55	0.44	0.43	0.43
UT	=~	co08	58.36	-0.54	-0.44	-0.42	-0.42
UT	=~	de05	52.89	0.67	0.54	0.47	0.47
UT	=~	fa06	47.51	0.42	0.34	0.32	0.32
UT	=~	fa02	37.75	-0.46	-0.37	-0.31	-0.31
UT	=~	co09	29.78	-0.31	-0.26	-0.24	-0.24
UT	=~	fa08	28.61	-0.38	-0.31	-0.28	-0.28
UT	=~	de02	20.41	-0.36	-0.30	-0.26	-0.26
UT	=~	co05	19.51	-0.25	-0.20	-0.18	-0.18
UT	=~	fa09	19.11	-0.31	-0.26	-0.22	-0.22
UT	=~	pr06	18.86	0.36	0.29	0.28	0.28
UT	=~	de09	17.71	0.43	0.35	0.29	0.29
UT	=~	un01	16.59	0.18	0.15	0.14	0.14
UT	=~	de07	14.55	0.29	0.23	0.23	0.23
UT	=~	un06	13.77	-0.25	-0.20	-0.16	-0.16
UT	=~	un10	10.65	-0.17	-0.14	-0.12	-0.12
UT	=~	un12	8.85	0.14	0.12	0.10	0.10
UT	=~	de03	7.09	-0.24	-0.19	-0.16	-0.16
UT	=~	un11	6.12	-0.12	-0.10	-0.08	-0.08
UT	=~	un03	5.78	0.15	0.12	0.10	0.10
UT	=~	de10	5.42	-0.19	-0.15	-0.13	-0.13
UT	=~	pr02	4.21	-0.15	-0.13	-0.13	-0.13
UT	=~	pr08	3.96	0.13	0.10	0.11	0.11
UT	=~	pr01	3.87	0.13	0.10	0.10	0.10
UT	=~	co01	3.54	0.11	0.09	0.08	0.08
UT	=~	fa04	3.16	-0.12	-0.10	-0.09	-0.09
UT	=~	un02	2.69	-0.07	-0.06	-0.05	-0.05
UT	=~	un05	2.66	0.07	0.06	0.05	0.05
UT	=~	de06	1.82	-0.12	-0.10	-0.08	-0.08
UT	=~	un08	1.31	0.06	0.05	0.04	0.04
UT	=~	pr05	1.12	-0.10	-0.08	-0.07	-0.07
UT	=~	fa01	0.93	0.05	0.04	0.04	0.04
UT	=~	pr10	0.86	0.08	0.06	0.06	0.06
UT	=~	un07	0.64	-0.04	-0.03	-0.03	-0.03
UT	=~	fa05	0.60	0.04	0.03	0.03	0.03
UT	=~	un09	0.49	-0.04	-0.03	-0.03	-0.03
UT	=~	co02	0.47	-0.04	-0.03	-0.03	-0.03
UT	=~	de01	0.40	-0.05	-0.04	-0.04	-0.04
UT	=~	fa10	0.35	0.04	0.03	0.03	0.03
UT	=~	pr07	0.27	-0.04	-0.03	-0.03	-0.03
UT	=~	un04	0.24	-0.02	-0.02	-0.02	-0.02
UT	=~	co06	0.23	-0.03	-0.03	-0.02	-0.02
UT	=~	de08	0.20	-0.03	-0.03	-0.03	-0.03
UT	=~	pr09	0.10	-0.02	-0.02	-0.02	-0.02
UT	=~	co10	0.01	0.00	0.00	0.00	0.00

Items correlations:

modif3 %>% 
  filter(op == "~~" & 
           !(lhs %in% c("PR", "CO", "UT", "FA", "DE", "UN")) &
           !(rhs %in% c("PR", "CO", "UT", "FA", "DE", "UN"))) %>% 
  kable(digits = 2,
        col.names = c("Item", "", "Item", "Modification Index", "epc", "sepc.lv", "sepc.all", "sepc.nox"))

Item		Item	Modification Index	epc	sepc.lv	sepc.all	sepc.nox
de05	~~	de09	182.77	0.70	0.70	0.63	0.63
pr05	~~	de06	168.01	0.58	0.58	0.62	0.62
fa02	~~	fa08	132.56	0.60	0.60	0.53	0.53
pr05	~~	ut11	125.45	0.54	0.54	0.52	0.52
fa02	~~	fa09	118.25	0.57	0.57	0.51	0.51
fa08	~~	fa09	105.17	0.52	0.52	0.48	0.48
ut11	~~	de06	100.33	0.47	0.47	0.47	0.47
ut03	~~	de09	59.71	0.39	0.39	0.35	0.35
pr06	~~	ut06	58.50	0.24	0.24	0.37	0.37
ut01	~~	ut02	55.84	0.17	0.17	0.44	0.44
fa02	~~	fa06	51.07	-0.32	-0.32	-0.34	-0.34
co08	~~	ut03	44.59	-0.29	-0.29	-0.31	-0.31
co08	~~	co09	43.60	0.24	0.24	0.33	0.33
ut03	~~	fa04	41.73	-0.29	-0.29	-0.30	-0.30
co04	~~	co08	41.47	-0.29	-0.29	-0.29	-0.29
ut07	~~	ut08	40.80	0.23	0.23	0.30	0.30
co08	~~	de09	40.13	-0.32	-0.32	-0.29	-0.29
co03	~~	co04	37.93	0.26	0.26	0.29	0.29
co04	~~	de10	37.10	-0.25	-0.25	-0.29	-0.29
ut06	~~	de07	36.79	0.17	0.17	0.30	0.30
pr06	~~	de07	35.62	0.20	0.20	0.28	0.28
co05	~~	co09	33.35	0.18	0.18	0.34	0.34
fa04	~~	un07	32.68	0.23	0.23	0.27	0.27
un01	~~	un08	32.62	0.15	0.15	0.29	0.29
co04	~~	un01	29.62	0.18	0.18	0.26	0.26
pr08	~~	de05	29.56	0.16	0.16	0.26	0.26
pr09	~~	de10	26.09	-0.16	-0.16	-0.25	-0.25
fa04	~~	de05	25.91	-0.24	-0.24	-0.24	-0.24
fa09	~~	un03	24.74	-0.24	-0.24	-0.23	-0.23
pr08	~~	de06	24.58	-0.15	-0.15	-0.24	-0.24
pr09	~~	ut08	24.00	0.15	0.15	0.23	0.23
co08	~~	fa04	23.88	0.22	0.22	0.22	0.22
fa04	~~	de03	23.44	0.22	0.22	0.23	0.23
fa04	~~	un10	23.32	0.19	0.19	0.23	0.23
co08	~~	un07	22.48	0.19	0.19	0.22	0.22
co10	~~	ut03	22.37	-0.19	-0.19	-0.22	-0.22
co03	~~	co08	21.95	-0.19	-0.19	-0.22	-0.22
co08	~~	un01	21.84	-0.15	-0.15	-0.22	-0.22
fa02	~~	fa10	21.72	0.23	0.23	0.22	0.22
fa04	~~	fa08	21.49	-0.23	-0.23	-0.21	-0.21
fa06	~~	fa08	21.28	-0.20	-0.20	-0.22	-0.22
co03	~~	un10	21.24	-0.16	-0.16	-0.22	-0.22
ut02	~~	un10	21.21	-0.11	-0.11	-0.24	-0.24
fa06	~~	fa10	21.14	-0.19	-0.19	-0.23	-0.23
fa04	~~	de09	21.00	-0.24	-0.24	-0.21	-0.21
de05	~~	un07	20.82	-0.18	-0.18	-0.22	-0.22
fa04	~~	fa09	20.42	-0.22	-0.22	-0.21	-0.21
co04	~~	co05	20.40	-0.16	-0.16	-0.23	-0.23
fa04	~~	un01	19.90	-0.15	-0.15	-0.21	-0.21
co08	~~	de10	19.79	0.17	0.17	0.21	0.21
fa09	~~	un06	19.23	0.23	0.23	0.20	0.20
co04	~~	ut01	18.96	0.14	0.14	0.21	0.21
pr07	~~	de02	18.78	0.14	0.14	0.22	0.22
de05	~~	un05	18.63	0.14	0.14	0.21	0.21
co03	~~	de07	18.33	0.14	0.14	0.20	0.20
fa02	~~	un03	18.30	-0.21	-0.21	-0.20	-0.20
co09	~~	de05	18.28	-0.15	-0.15	-0.21	-0.21
fa04	~~	un12	18.25	-0.16	-0.16	-0.20	-0.20
ut03	~~	fa09	18.22	0.20	0.20	0.20	0.20
co04	~~	un07	18.15	-0.17	-0.17	-0.20	-0.20
fa06	~~	fa09	18.03	-0.18	-0.18	-0.20	-0.20
ut01	~~	de05	18.00	0.14	0.14	0.21	0.21
co04	~~	de05	17.94	0.20	0.20	0.20	0.20
ut11	~~	un09	17.94	0.19	0.19	0.20	0.20
co05	~~	co08	17.89	0.15	0.15	0.22	0.22
co04	~~	co09	17.73	-0.16	-0.16	-0.21	-0.21
de09	~~	un07	17.50	-0.19	-0.19	-0.19	-0.19
de05	~~	un10	17.02	-0.16	-0.16	-0.20	-0.20
fa04	~~	un08	16.99	-0.15	-0.15	-0.20	-0.20
de05	~~	un12	16.99	0.15	0.15	0.20	0.20
co08	~~	fa09	16.13	-0.19	-0.19	-0.19	-0.19
fa08	~~	un03	15.93	-0.19	-0.19	-0.18	-0.18
ut01	~~	fa04	15.86	-0.13	-0.13	-0.19	-0.19
co09	~~	fa04	15.60	0.14	0.14	0.19	0.19
pr01	~~	un01	15.38	0.09	0.09	0.20	0.20
fa10	~~	un08	15.35	0.14	0.14	0.19	0.19
pr05	~~	de01	15.31	-0.16	-0.16	-0.18	-0.18
ut01	~~	un07	15.31	-0.11	-0.11	-0.19	-0.19
co04	~~	ut03	15.13	0.18	0.18	0.18	0.18
ut01	~~	ut03	15.09	0.12	0.12	0.19	0.19
co04	~~	fa04	15.01	-0.18	-0.18	-0.18	-0.18
co06	~~	un10	14.83	0.13	0.13	0.19	0.19
un07	~~	un09	14.63	0.14	0.14	0.18	0.18
ut03	~~	de05	14.57	0.17	0.17	0.18	0.18
co08	~~	de05	14.56	-0.17	-0.17	-0.18	-0.18
de01	~~	de09	14.39	-0.18	-0.18	-0.18	-0.18
un01	~~	un10	14.28	-0.11	-0.11	-0.19	-0.19
co04	~~	ut04	14.14	0.17	0.17	0.17	0.17
fa02	~~	fa04	14.14	-0.19	-0.19	-0.17	-0.17
un07	~~	un10	14.10	0.13	0.13	0.18	0.18
pr09	~~	de01	13.97	0.12	0.12	0.18	0.18
fa10	~~	un07	13.93	-0.15	-0.15	-0.18	-0.18
fa04	~~	de02	13.83	0.15	0.15	0.18	0.18
pr08	~~	de09	13.70	0.13	0.13	0.18	0.18
fa09	~~	de09	13.29	0.20	0.20	0.17	0.17
pr08	~~	co03	13.19	0.10	0.10	0.18	0.18
pr05	~~	de05	13.00	-0.16	-0.16	-0.17	-0.17
ut03	~~	fa08	12.94	0.17	0.17	0.17	0.17
fa06	~~	un08	12.84	-0.12	-0.12	-0.17	-0.17
de02	~~	un01	12.71	-0.10	-0.10	-0.18	-0.18
fa04	~~	de10	12.65	0.14	0.14	0.17	0.17
de03	~~	de10	12.61	0.14	0.14	0.18	0.18
ut01	~~	un09	12.54	-0.10	-0.10	-0.18	-0.18
ut03	~~	un10	12.50	-0.13	-0.13	-0.17	-0.17
co06	~~	un01	12.38	-0.11	-0.11	-0.17	-0.17
co01	~~	co06	12.28	-0.12	-0.12	-0.19	-0.19
pr09	~~	ut12	12.14	0.11	0.11	0.17	0.17
de03	~~	de05	12.11	-0.16	-0.16	-0.17	-0.17
de01	~~	de06	12.08	-0.15	-0.15	-0.17	-0.17
ut01	~~	ut11	12.07	-0.12	-0.12	-0.17	-0.17
fa09	~~	un07	11.93	-0.14	-0.14	-0.16	-0.16
co04	~~	un10	11.92	-0.13	-0.13	-0.16	-0.16
co08	~~	un10	11.81	0.13	0.13	0.16	0.16
de03	~~	de09	11.78	-0.17	-0.17	-0.16	-0.16
fa09	~~	fa10	11.75	0.16	0.16	0.16	0.16
de09	~~	de10	11.64	-0.16	-0.16	-0.16	-0.16
ut05	~~	un10	11.63	0.12	0.12	0.16	0.16
co04	~~	un05	11.63	0.11	0.11	0.17	0.17
co04	~~	de09	11.55	0.18	0.18	0.15	0.15
un01	~~	un05	11.54	0.08	0.08	0.18	0.18
de05	~~	de10	11.50	-0.14	-0.14	-0.17	-0.17
ut02	~~	un12	11.44	0.08	0.08	0.18	0.18
pr05	~~	un01	11.41	-0.11	-0.11	-0.16	-0.16
co06	~~	fa04	11.20	0.14	0.14	0.16	0.16
pr08	~~	ut02	11.18	0.06	0.06	0.18	0.18
co10	~~	fa08	11.16	-0.14	-0.14	-0.16	-0.16
un02	~~	un06	11.11	0.11	0.11	0.17	0.17
pr01	~~	un04	11.08	0.08	0.08	0.17	0.17
co10	~~	un10	11.07	0.11	0.11	0.16	0.16
pr01	~~	co08	11.01	-0.10	-0.10	-0.16	-0.16
co05	~~	ut01	10.94	-0.08	-0.08	-0.18	-0.18
pr01	~~	de10	10.93	-0.09	-0.09	-0.17	-0.17
de09	~~	un06	10.79	0.18	0.18	0.15	0.15
ut02	~~	ut03	10.62	0.10	0.10	0.17	0.17
ut03	~~	fa02	10.62	0.16	0.16	0.15	0.15
de09	~~	un05	10.57	0.12	0.12	0.16	0.16
pr07	~~	ut09	10.47	0.11	0.11	0.16	0.16
ut08	~~	de01	10.41	0.11	0.11	0.15	0.15
pr01	~~	pr09	10.40	0.08	0.08	0.17	0.17
co09	~~	ut03	10.15	-0.11	-0.11	-0.16	-0.16
pr01	~~	ut03	10.12	0.09	0.09	0.16	0.16
de10	~~	un08	10.11	-0.10	-0.10	-0.16	-0.16
fa06	~~	de02	10.07	0.11	0.11	0.16	0.16
fa08	~~	fa10	10.03	0.15	0.15	0.15	0.15
un05	~~	un09	9.91	-0.09	-0.09	-0.16	-0.16
pr07	~~	de06	9.90	-0.11	-0.11	-0.16	-0.16
ut03	~~	un06	9.84	0.15	0.15	0.14	0.14
ut02	~~	de05	9.82	0.09	0.09	0.16	0.16
ut01	~~	fa10	9.66	0.10	0.10	0.16	0.16
co06	~~	un09	9.62	0.12	0.12	0.15	0.15
ut08	~~	ut11	9.59	-0.13	-0.13	-0.15	-0.15
un08	~~	un11	9.53	0.09	0.09	0.16	0.16
co08	~~	un05	9.50	-0.09	-0.09	-0.15	-0.15
pr09	~~	un11	9.49	-0.09	-0.09	-0.15	-0.15
de08	~~	un04	9.36	-0.08	-0.08	-0.15	-0.15
fa06	~~	un10	9.21	0.10	0.10	0.15	0.15
fa02	~~	un06	9.20	0.16	0.16	0.14	0.14
pr06	~~	de03	9.08	-0.12	-0.12	-0.14	-0.14
co05	~~	un04	9.05	0.08	0.08	0.16	0.16
pr06	~~	un08	8.90	-0.10	-0.10	-0.14	-0.14
de02	~~	de05	8.90	-0.12	-0.12	-0.15	-0.15
co04	~~	un12	8.90	0.11	0.11	0.14	0.14
fa04	~~	fa06	8.87	0.13	0.13	0.14	0.14
de10	~~	un01	8.79	-0.09	-0.09	-0.15	-0.15
fa05	~~	de09	8.77	-0.11	-0.11	-0.16	-0.16
pr05	~~	un09	8.75	0.12	0.12	0.14	0.14
un02	~~	un12	8.74	-0.07	-0.07	-0.16	-0.16
co04	~~	de02	8.58	-0.12	-0.12	-0.14	-0.14
co03	~~	co05	8.49	-0.09	-0.09	-0.15	-0.15
ut02	~~	ut12	8.49	-0.08	-0.08	-0.16	-0.16
de09	~~	un10	8.47	-0.13	-0.13	-0.14	-0.14
de05	~~	un08	8.44	0.10	0.10	0.14	0.14
co06	~~	ut09	8.43	0.11	0.11	0.14	0.14
pr01	~~	fa06	8.37	0.08	0.08	0.15	0.15
pr06	~~	de02	8.31	-0.10	-0.10	-0.14	-0.14
pr01	~~	fa04	8.29	-0.09	-0.09	-0.14	-0.14
co08	~~	ut02	8.29	-0.08	-0.08	-0.15	-0.15
pr08	~~	un07	8.25	-0.08	-0.08	-0.14	-0.14
pr09	~~	un05	8.21	0.07	0.07	0.14	0.14
ut01	~~	ut09	8.19	-0.09	-0.09	-0.15	-0.15
fa04	~~	fa10	8.19	-0.13	-0.13	-0.14	-0.14
co03	~~	fa04	8.17	-0.12	-0.12	-0.13	-0.13
pr05	~~	ut12	8.15	-0.11	-0.11	-0.14	-0.14
de06	~~	de10	8.14	0.11	0.11	0.14	0.14
co03	~~	de03	8.12	-0.11	-0.11	-0.13	-0.13
co03	~~	ut03	8.10	0.11	0.11	0.13	0.13
pr08	~~	de03	8.04	-0.08	-0.08	-0.14	-0.14
pr05	~~	de10	7.93	0.11	0.11	0.14	0.14
co09	~~	un07	7.88	0.09	0.09	0.14	0.14
ut11	~~	fa06	7.81	0.12	0.12	0.13	0.13
fa01	~~	un01	7.75	0.07	0.07	0.15	0.15
un01	~~	un07	7.72	-0.08	-0.08	-0.14	-0.14
co04	~~	un08	7.69	0.10	0.10	0.13	0.13
ut09	~~	un09	7.63	0.10	0.10	0.13	0.13
fa02	~~	un10	7.62	-0.11	-0.11	-0.13	-0.13
fa01	~~	un10	7.62	-0.07	-0.07	-0.15	-0.15
de03	~~	un10	7.62	0.10	0.10	0.13	0.13
pr01	~~	pr05	7.61	-0.09	-0.09	-0.14	-0.14
ut01	~~	un01	7.60	0.06	0.06	0.14	0.14
fa10	~~	un01	7.58	0.09	0.09	0.13	0.13
ut11	~~	de05	7.58	-0.13	-0.13	-0.13	-0.13
pr05	~~	ut01	7.56	-0.09	-0.09	-0.14	-0.14
un02	~~	un03	7.43	0.08	0.08	0.14	0.14
fa10	~~	de02	7.43	-0.11	-0.11	-0.13	-0.13
ut01	~~	fa06	7.42	-0.08	-0.08	-0.14	-0.14
de03	~~	de06	7.40	0.12	0.12	0.13	0.13
ut02	~~	fa04	7.36	-0.08	-0.08	-0.14	-0.14
ut04	~~	ut07	7.36	-0.12	-0.12	-0.13	-0.13
co09	~~	un12	7.27	-0.08	-0.08	-0.14	-0.14
co09	~~	un06	7.24	0.10	0.10	0.13	0.13
un02	~~	un04	7.23	0.07	0.07	0.14	0.14
ut01	~~	ut05	7.15	0.08	0.08	0.14	0.14
co01	~~	ut12	7.07	0.08	0.08	0.13	0.13
ut07	~~	de06	7.05	-0.11	-0.11	-0.13	-0.13
pr09	~~	fa04	7.03	-0.10	-0.10	-0.12	-0.12
co03	~~	un08	6.77	0.08	0.08	0.12	0.12
co03	~~	ut11	6.76	0.11	0.11	0.12	0.12
pr09	~~	ut11	6.75	-0.10	-0.10	-0.12	-0.12
co04	~~	un06	6.74	-0.13	-0.13	-0.12	-0.12
pr08	~~	fa08	6.71	-0.08	-0.08	-0.12	-0.12
pr02	~~	ut03	6.69	-0.09	-0.09	-0.12	-0.12
ut11	~~	de01	6.69	-0.12	-0.12	-0.12	-0.12
pr06	~~	co04	6.67	0.11	0.11	0.12	0.12
fa05	~~	fa09	6.64	-0.09	-0.09	-0.15	-0.15
pr02	~~	de06	6.61	0.09	0.09	0.12	0.12
co01	~~	de10	6.59	0.08	0.08	0.13	0.13
de10	~~	un09	6.56	0.09	0.09	0.13	0.13
ut11	~~	fa04	6.52	0.13	0.13	0.12	0.12
co04	~~	de07	6.50	0.10	0.10	0.12	0.12
co08	~~	ut05	6.49	0.11	0.11	0.12	0.12
fa04	~~	un09	6.44	0.11	0.11	0.12	0.12
pr08	~~	un02	6.42	-0.05	-0.05	-0.13	-0.13
ut02	~~	ut04	6.37	0.07	0.07	0.13	0.13
un05	~~	un07	6.33	-0.07	-0.07	-0.13	-0.13
ut12	~~	de03	6.33	0.10	0.10	0.12	0.12
ut02	~~	un03	6.32	0.07	0.07	0.13	0.13
co05	~~	de05	6.26	-0.09	-0.09	-0.13	-0.13
co09	~~	de06	6.25	0.09	0.09	0.13	0.13
pr06	~~	ut11	6.25	-0.11	-0.11	-0.12	-0.12
fa10	~~	un02	6.23	-0.08	-0.08	-0.13	-0.13
co03	~~	de05	6.17	0.10	0.10	0.12	0.12
ut01	~~	de02	6.10	-0.07	-0.07	-0.13	-0.13
pr09	~~	ut07	6.07	0.08	0.08	0.12	0.12
fa08	~~	de08	6.04	0.09	0.09	0.12	0.12
pr05	~~	co04	6.03	-0.11	-0.11	-0.11	-0.11
ut06	~~	de02	6.03	-0.07	-0.07	-0.12	-0.12
fa08	~~	un06	6.00	0.13	0.13	0.11	0.11
fa09	~~	de07	6.00	-0.10	-0.10	-0.12	-0.12
de03	~~	un12	5.96	-0.09	-0.09	-0.12	-0.12
co08	~~	un11	5.95	0.09	0.09	0.12	0.12
co02	~~	ut09	5.93	-0.08	-0.08	-0.12	-0.12
ut07	~~	ut09	5.92	-0.10	-0.10	-0.12	-0.12
co03	~~	de08	5.92	0.08	0.08	0.12	0.12
pr05	~~	ut08	5.91	-0.09	-0.09	-0.12	-0.12
pr01	~~	co04	5.85	0.07	0.07	0.12	0.12
de06	~~	un01	5.84	-0.08	-0.08	-0.12	-0.12
co08	~~	de03	5.81	0.10	0.10	0.11	0.11
de03	~~	un02	5.79	0.07	0.07	0.12	0.12
co06	~~	ut02	5.78	-0.06	-0.06	-0.13	-0.13
de06	~~	un09	5.76	0.10	0.10	0.12	0.12
ut03	~~	un07	5.74	-0.09	-0.09	-0.11	-0.11
co01	~~	un01	5.73	0.06	0.06	0.12	0.12
ut01	~~	un05	5.71	0.05	0.05	0.12	0.12
ut03	~~	de01	5.70	-0.10	-0.10	-0.11	-0.11
pr05	~~	fa08	5.68	0.11	0.11	0.11	0.11
pr10	~~	ut04	5.68	-0.10	-0.10	-0.11	-0.11
ut05	~~	de10	5.68	0.09	0.09	0.12	0.12
un08	~~	un10	5.67	-0.07	-0.07	-0.12	-0.12
de05	~~	un11	5.66	-0.09	-0.09	-0.11	-0.11
de05	~~	un06	5.66	-0.12	-0.12	-0.11	-0.11
ut02	~~	un07	5.64	-0.06	-0.06	-0.12	-0.12
un08	~~	un09	5.63	-0.08	-0.08	-0.12	-0.12
co09	~~	de02	5.61	0.07	0.07	0.12	0.12
co05	~~	un11	5.59	-0.07	-0.07	-0.12	-0.12
ut09	~~	ut11	5.58	0.10	0.10	0.11	0.11
fa08	~~	de09	5.57	0.13	0.13	0.11	0.11
co01	~~	fa05	5.57	0.06	0.06	0.14	0.14
co06	~~	co10	5.54	0.09	0.09	0.12	0.12
ut07	~~	fa08	5.53	-0.11	-0.11	-0.11	-0.11
de02	~~	un07	5.52	0.08	0.08	0.11	0.11
pr05	~~	un07	5.46	0.09	0.09	0.11	0.11
ut11	~~	de10	5.45	0.10	0.10	0.11	0.11
ut04	~~	un06	5.42	-0.11	-0.11	-0.11	-0.11
co08	~~	fa08	5.41	-0.11	-0.11	-0.11	-0.11
co04	~~	fa02	5.41	-0.12	-0.12	-0.11	-0.11
pr05	~~	un05	5.41	-0.07	-0.07	-0.11	-0.11
co06	~~	fa10	5.39	-0.09	-0.09	-0.11	-0.11
pr09	~~	co04	5.38	0.08	0.08	0.11	0.11
ut12	~~	de01	5.38	0.08	0.08	0.11	0.11
co08	~~	un06	5.26	-0.11	-0.11	-0.11	-0.11
co01	~~	ut04	5.22	-0.08	-0.08	-0.11	-0.11
ut01	~~	un03	5.22	0.07	0.07	0.11	0.11
pr09	~~	de08	5.21	0.07	0.07	0.11	0.11
co09	~~	ut04	5.19	-0.08	-0.08	-0.11	-0.11
pr05	~~	pr08	5.17	-0.07	-0.07	-0.11	-0.11
co08	~~	co10	5.15	0.09	0.09	0.11	0.11
ut05	~~	un07	5.15	0.08	0.08	0.11	0.11
pr02	~~	pr05	5.15	0.08	0.08	0.11	0.11
co04	~~	fa10	5.12	0.10	0.10	0.11	0.11
co01	~~	de01	5.09	0.07	0.07	0.11	0.11
ut08	~~	de10	5.06	-0.07	-0.07	-0.11	-0.11
pr05	~~	ut07	5.05	-0.10	-0.10	-0.11	-0.11
co08	~~	fa05	5.05	0.07	0.07	0.12	0.12
pr07	~~	ut01	5.04	-0.06	-0.06	-0.11	-0.11
pr10	~~	co06	5.04	-0.08	-0.08	-0.11	-0.11
co03	~~	un01	5.02	0.07	0.07	0.11	0.11
co03	~~	ut04	5.02	0.09	0.09	0.10	0.10
ut03	~~	un04	4.99	-0.08	-0.08	-0.11	-0.11
ut12	~~	fa06	4.97	0.08	0.08	0.11	0.11
ut05	~~	fa09	4.94	-0.10	-0.10	-0.11	-0.11
pr02	~~	de01	4.94	-0.07	-0.07	-0.11	-0.11
ut04	~~	de08	4.92	0.08	0.08	0.11	0.11
co10	~~	fa09	4.90	-0.10	-0.10	-0.10	-0.10
ut01	~~	fa05	4.89	0.05	0.05	0.13	0.13
pr05	~~	fa04	4.88	0.10	0.10	0.10	0.10
pr08	~~	de02	4.86	-0.06	-0.06	-0.11	-0.11
un07	~~	un12	4.86	-0.07	-0.07	-0.11	-0.11
fa09	~~	un11	4.86	-0.08	-0.08	-0.11	-0.11
fa06	~~	un07	4.83	0.08	0.08	0.11	0.11
co04	~~	ut02	4.82	0.07	0.07	0.11	0.11
co04	~~	un09	4.80	-0.09	-0.09	-0.10	-0.10
co03	~~	de09	4.80	0.10	0.10	0.10	0.10
pr05	~~	co09	4.79	0.08	0.08	0.11	0.11
ut07	~~	fa10	4.76	0.09	0.09	0.10	0.10
ut03	~~	de10	4.74	-0.08	-0.08	-0.10	-0.10
fa06	~~	un03	4.71	0.09	0.09	0.10	0.10
fa10	~~	un10	4.70	-0.08	-0.08	-0.10	-0.10
fa04	~~	un05	4.65	-0.07	-0.07	-0.10	-0.10
fa05	~~	de10	4.63	0.06	0.06	0.12	0.12
fa04	~~	de06	4.63	0.10	0.10	0.10	0.10
fa08	~~	un04	4.62	-0.08	-0.08	-0.10	-0.10
co02	~~	ut05	4.60	0.07	0.07	0.11	0.11
fa05	~~	de03	4.58	0.07	0.07	0.12	0.12
ut02	~~	ut08	4.58	-0.05	-0.05	-0.12	-0.12
ut04	~~	fa08	4.57	0.10	0.10	0.10	0.10
co03	~~	co06	4.55	-0.08	-0.08	-0.10	-0.10
pr02	~~	co06	4.54	0.07	0.07	0.10	0.10
de01	~~	de03	4.54	0.09	0.09	0.10	0.10
co09	~~	un05	4.52	-0.05	-0.05	-0.11	-0.11
pr10	~~	co02	4.51	0.07	0.07	0.10	0.10
de05	~~	un03	4.50	0.10	0.10	0.10	0.10
fa10	~~	de05	4.49	0.09	0.09	0.10	0.10
pr09	~~	un01	4.47	0.06	0.06	0.10	0.10
co03	~~	fa01	4.45	0.06	0.06	0.11	0.11
un03	~~	un08	4.43	-0.08	-0.08	-0.10	-0.10
co05	~~	un01	4.40	-0.05	-0.05	-0.11	-0.11
co03	~~	de02	4.39	-0.07	-0.07	-0.10	-0.10
pr08	~~	un01	4.37	0.05	0.05	0.10	0.10
pr08	~~	co04	4.37	0.06	0.06	0.10	0.10
de02	~~	un10	4.37	0.07	0.07	0.10	0.10
fa01	~~	fa08	4.36	-0.07	-0.07	-0.12	-0.12
pr09	~~	ut06	4.34	-0.06	-0.06	-0.10	-0.10
pr01	~~	co09	4.34	-0.05	-0.05	-0.11	-0.11
co05	~~	de01	4.31	0.07	0.07	0.11	0.11
ut07	~~	de01	4.29	0.08	0.08	0.10	0.10
pr06	~~	fa01	4.29	-0.06	-0.06	-0.11	-0.11
ut04	~~	de07	4.28	0.08	0.08	0.10	0.10
de10	~~	un10	4.28	0.07	0.07	0.10	0.10
de01	~~	de05	4.27	-0.09	-0.09	-0.10	-0.10
ut02	~~	de03	4.25	-0.06	-0.06	-0.11	-0.11
de01	~~	un08	4.23	0.07	0.07	0.10	0.10
pr05	~~	un06	4.19	0.10	0.10	0.10	0.10
co06	~~	co09	4.19	0.07	0.07	0.11	0.11
pr08	~~	ut11	4.18	-0.06	-0.06	-0.10	-0.10
de06	~~	un07	4.16	0.08	0.08	0.10	0.10
pr10	~~	fa02	4.15	-0.09	-0.09	-0.09	-0.09
un06	~~	un08	4.14	-0.08	-0.08	-0.10	-0.10
de05	~~	un02	4.12	-0.06	-0.06	-0.10	-0.10
ut06	~~	un05	4.12	0.05	0.05	0.10	0.10
de02	~~	de07	4.06	-0.07	-0.07	-0.10	-0.10
fa02	~~	de06	4.05	0.10	0.10	0.09	0.09
co05	~~	un07	4.04	0.06	0.06	0.10	0.10
ut06	~~	de08	4.03	-0.06	-0.06	-0.10	-0.10
pr07	~~	ut07	4.03	0.07	0.07	0.10	0.10
ut02	~~	fa06	4.02	-0.05	-0.05	-0.10	-0.10
co10	~~	un01	3.97	-0.06	-0.06	-0.10	-0.10
de02	~~	de10	3.96	0.07	0.07	0.10	0.10
de01	~~	de02	3.96	0.07	0.07	0.10	0.10
fa04	~~	de07	3.95	-0.08	-0.08	-0.09	-0.09
pr02	~~	co10	3.93	0.06	0.06	0.09	0.09
co06	~~	fa08	3.93	-0.09	-0.09	-0.09	-0.09
de10	~~	un03	3.90	-0.08	-0.08	-0.10	-0.10
ut09	~~	fa05	3.90	-0.06	-0.06	-0.11	-0.11
de02	~~	de09	3.90	-0.09	-0.09	-0.09	-0.09
pr06	~~	co05	3.90	-0.06	-0.06	-0.10	-0.10
pr08	~~	co08	3.88	-0.06	-0.06	-0.09	-0.09
pr07	~~	de10	3.87	0.06	0.06	0.10	0.10
de06	~~	un05	3.86	-0.06	-0.06	-0.10	-0.10
un04	~~	un08	3.85	-0.06	-0.06	-0.10	-0.10
pr02	~~	ut04	3.85	0.07	0.07	0.09	0.09
de02	~~	un12	3.85	-0.06	-0.06	-0.10	-0.10
ut05	~~	de01	3.82	0.08	0.08	0.09	0.09
co04	~~	un04	3.81	-0.07	-0.07	-0.09	-0.09
fa09	~~	un02	3.77	0.06	0.06	0.10	0.10
de09	~~	un12	3.76	0.08	0.08	0.09	0.09
ut03	~~	ut07	3.76	-0.08	-0.08	-0.09	-0.09
co03	~~	co09	3.76	-0.06	-0.06	-0.10	-0.10
co02	~~	ut02	3.75	-0.05	-0.05	-0.10	-0.10
de05	~~	un01	3.74	0.06	0.06	0.09	0.09
fa09	~~	un10	3.73	-0.08	-0.08	-0.09	-0.09
co06	~~	fa02	3.73	-0.09	-0.09	-0.09	-0.09
pr01	~~	un11	3.72	-0.05	-0.05	-0.10	-0.10
de07	~~	un06	3.72	-0.08	-0.08	-0.09	-0.09
ut02	~~	de09	3.71	0.06	0.06	0.10	0.10
de01	~~	un02	3.71	0.05	0.05	0.10	0.10
ut05	~~	un11	3.69	0.07	0.07	0.09	0.09
de03	~~	de07	3.68	-0.07	-0.07	-0.09	-0.09
co09	~~	de09	3.67	-0.08	-0.08	-0.09	-0.09
fa02	~~	de10	3.66	0.08	0.08	0.09	0.09
ut02	~~	de07	3.65	-0.05	-0.05	-0.10	-0.10
un03	~~	un05	3.62	0.06	0.06	0.09	0.09
ut09	~~	de03	3.60	-0.08	-0.08	-0.09	-0.09
co02	~~	fa01	3.59	0.05	0.05	0.11	0.11
pr10	~~	co10	3.59	0.07	0.07	0.09	0.09
de01	~~	de08	3.59	0.06	0.06	0.09	0.09
ut04	~~	un11	3.59	-0.07	-0.07	-0.09	-0.09
co04	~~	ut05	3.55	-0.08	-0.08	-0.09	-0.09
un06	~~	un10	3.53	0.08	0.08	0.09	0.09
ut07	~~	ut11	3.53	-0.09	-0.09	-0.09	-0.09
fa02	~~	un08	3.53	0.07	0.07	0.09	0.09
un06	~~	un12	3.52	-0.07	-0.07	-0.09	-0.09
un01	~~	un12	3.52	0.05	0.05	0.09	0.09
de05	~~	de06	3.50	-0.08	-0.08	-0.09	-0.09
co06	~~	un03	3.48	0.08	0.08	0.09	0.09
co06	~~	fa01	3.47	-0.05	-0.05	-0.10	-0.10
pr01	~~	ut12	3.45	0.05	0.05	0.09	0.09
ut08	~~	de07	3.43	-0.06	-0.06	-0.09	-0.09
fa01	~~	fa10	3.42	0.07	0.07	0.12	0.12
co01	~~	de03	3.42	0.06	0.06	0.09	0.09
un02	~~	un08	3.41	-0.05	-0.05	-0.10	-0.10
ut03	~~	un09	3.41	-0.07	-0.07	-0.09	-0.09
pr05	~~	fa05	3.41	-0.06	-0.06	-0.10	-0.10
pr02	~~	fa04	3.41	0.07	0.07	0.09	0.09
fa06	~~	un01	3.39	-0.05	-0.05	-0.09	-0.09
ut01	~~	de08	3.38	-0.05	-0.05	-0.09	-0.09
pr05	~~	un10	3.38	0.07	0.07	0.09	0.09
pr09	~~	un08	3.36	0.05	0.05	0.09	0.09
ut01	~~	fa08	3.36	-0.06	-0.06	-0.09	-0.09
ut01	~~	ut04	3.36	0.06	0.06	0.09	0.09
co10	~~	un08	3.36	0.06	0.06	0.09	0.09
fa02	~~	de09	3.36	0.10	0.10	0.08	0.08
ut03	~~	de08	3.35	0.06	0.06	0.09	0.09
pr08	~~	pr10	3.34	0.05	0.05	0.09	0.09
un04	~~	un06	3.33	0.07	0.07	0.09	0.09
co09	~~	de01	3.31	-0.06	-0.06	-0.09	-0.09
pr09	~~	de02	3.31	0.06	0.06	0.09	0.09
co01	~~	ut08	3.27	0.05	0.05	0.09	0.09
pr07	~~	un12	3.26	-0.05	-0.05	-0.09	-0.09
ut12	~~	de07	3.25	-0.06	-0.06	-0.09	-0.09
co03	~~	fa05	3.24	-0.05	-0.05	-0.10	-0.10
co06	~~	fa06	3.23	0.07	0.07	0.09	0.09
fa05	~~	un10	3.21	0.05	0.05	0.10	0.10
un07	~~	un08	3.20	-0.06	-0.06	-0.09	-0.09
ut01	~~	un12	3.19	0.05	0.05	0.09	0.09
fa04	~~	fa05	3.18	0.06	0.06	0.10	0.10
ut01	~~	de06	3.17	-0.06	-0.06	-0.09	-0.09
ut03	~~	un05	3.17	0.05	0.05	0.09	0.09
pr09	~~	co08	3.14	-0.06	-0.06	-0.08	-0.08
pr09	~~	fa08	3.14	0.07	0.07	0.08	0.08
pr02	~~	co02	3.13	0.05	0.05	0.09	0.09
pr05	~~	pr09	3.12	-0.06	-0.06	-0.08	-0.08
ut03	~~	ut08	3.11	-0.07	-0.07	-0.08	-0.08
pr07	~~	un01	3.11	-0.05	-0.05	-0.09	-0.09
ut01	~~	de01	3.10	-0.05	-0.05	-0.09	-0.09
ut06	~~	fa02	3.08	-0.07	-0.07	-0.08	-0.08
de09	~~	un11	3.05	-0.07	-0.07	-0.08	-0.08
ut09	~~	de07	3.04	0.06	0.06	0.08	0.08
de02	~~	un05	3.00	-0.05	-0.05	-0.09	-0.09
fa08	~~	de05	3.00	-0.08	-0.08	-0.08	-0.08
pr06	~~	de08	3.00	-0.06	-0.06	-0.08	-0.08
pr07	~~	de05	2.99	-0.06	-0.06	-0.08	-0.08
ut07	~~	un07	2.99	0.06	0.06	0.08	0.08
fa06	~~	un09	2.98	0.06	0.06	0.08	0.08
fa05	~~	un06	2.98	-0.06	-0.06	-0.09	-0.09
ut12	~~	un09	2.97	-0.06	-0.06	-0.08	-0.08
ut04	~~	fa04	2.97	-0.08	-0.08	-0.08	-0.08
ut08	~~	de02	2.97	0.06	0.06	0.08	0.08
co02	~~	un10	2.96	0.05	0.05	0.08	0.08
pr01	~~	de05	2.96	0.05	0.05	0.09	0.09
ut04	~~	un08	2.95	0.06	0.06	0.08	0.08
ut09	~~	fa04	2.93	0.07	0.07	0.08	0.08
ut03	~~	de03	2.93	-0.07	-0.07	-0.08	-0.08
co09	~~	de10	2.92	0.05	0.05	0.09	0.09
de03	~~	un11	2.92	-0.06	-0.06	-0.08	-0.08
ut08	~~	un08	2.92	0.05	0.05	0.08	0.08
ut12	~~	un03	2.92	-0.07	-0.07	-0.08	-0.08
ut05	~~	ut08	2.91	-0.06	-0.06	-0.08	-0.08
pr05	~~	co01	2.91	-0.06	-0.06	-0.08	-0.08
fa05	~~	de05	2.91	-0.05	-0.05	-0.09	-0.09
de07	~~	un09	2.91	0.06	0.06	0.08	0.08
pr09	~~	ut05	2.88	-0.06	-0.06	-0.08	-0.08
ut07	~~	un06	2.88	-0.08	-0.08	-0.08	-0.08
un04	~~	un12	2.87	0.05	0.05	0.08	0.08
pr05	~~	fa09	2.87	0.08	0.08	0.08	0.08
pr08	~~	un08	2.86	0.04	0.04	0.08	0.08
ut08	~~	fa06	2.83	0.06	0.06	0.08	0.08
fa10	~~	un06	2.82	-0.08	-0.08	-0.08	-0.08
pr02	~~	fa02	2.82	-0.07	-0.07	-0.08	-0.08
pr01	~~	un09	2.82	-0.05	-0.05	-0.08	-0.08
ut04	~~	ut05	2.82	-0.07	-0.07	-0.08	-0.08
co09	~~	un01	2.81	-0.04	-0.04	-0.08	-0.08
ut06	~~	un08	2.81	-0.05	-0.05	-0.08	-0.08
fa08	~~	de06	2.81	0.08	0.08	0.08	0.08
de02	~~	un11	2.81	0.05	0.05	0.08	0.08
ut12	~~	un01	2.81	0.05	0.05	0.08	0.08
pr05	~~	fa02	2.80	0.08	0.08	0.08	0.08
pr10	~~	un09	2.80	-0.06	-0.06	-0.08	-0.08
pr01	~~	co05	2.80	0.04	0.04	0.09	0.09
ut11	~~	un01	2.79	-0.06	-0.06	-0.08	-0.08
ut07	~~	un09	2.79	-0.07	-0.07	-0.08	-0.08
pr07	~~	pr09	2.78	-0.05	-0.05	-0.08	-0.08
fa05	~~	un05	2.77	0.04	0.04	0.10	0.10
co02	~~	un09	2.77	-0.06	-0.06	-0.08	-0.08
de02	~~	un09	2.76	0.06	0.06	0.08	0.08
fa01	~~	de07	2.75	0.04	0.04	0.09	0.09
co10	~~	ut01	2.75	0.05	0.05	0.08	0.08
fa10	~~	un09	2.74	-0.07	-0.07	-0.08	-0.08
co10	~~	de10	2.74	-0.06	-0.06	-0.08	-0.08
co02	~~	un12	2.74	-0.05	-0.05	-0.08	-0.08
ut03	~~	un12	2.74	0.06	0.06	0.08	0.08
un01	~~	un11	2.73	-0.05	-0.05	-0.08	-0.08
pr07	~~	co08	2.70	0.06	0.06	0.08	0.08
co05	~~	co10	2.70	-0.05	-0.05	-0.09	-0.09
pr07	~~	co09	2.69	0.05	0.05	0.08	0.08
co10	~~	fa02	2.68	-0.07	-0.07	-0.08	-0.08
ut11	~~	fa10	2.68	-0.08	-0.08	-0.08	-0.08
un09	~~	un11	2.68	0.06	0.06	0.08	0.08
pr02	~~	co05	2.68	-0.05	-0.05	-0.08	-0.08
fa05	~~	de01	2.67	0.05	0.05	0.09	0.09
un02	~~	un07	2.67	0.05	0.05	0.08	0.08
co06	~~	ut12	2.67	-0.06	-0.06	-0.08	-0.08
ut02	~~	un01	2.67	0.03	0.03	0.09	0.09
fa09	~~	de06	2.67	0.08	0.08	0.08	0.08
de02	~~	de03	2.66	0.06	0.06	0.08	0.08
de08	~~	un01	2.66	0.04	0.04	0.08	0.08
co03	~~	un06	2.66	-0.07	-0.07	-0.08	-0.08
co03	~~	ut09	2.66	0.06	0.06	0.08	0.08
fa02	~~	un09	2.65	-0.07	-0.07	-0.08	-0.08
co09	~~	de07	2.63	-0.05	-0.05	-0.08	-0.08
ut02	~~	fa02	2.63	0.05	0.05	0.08	0.08
co03	~~	un12	2.62	0.05	0.05	0.08	0.08
de10	~~	un07	2.59	0.06	0.06	0.08	0.08
ut08	~~	fa05	2.59	-0.04	-0.04	-0.09	-0.09
ut01	~~	de10	2.59	-0.05	-0.05	-0.08	-0.08
co03	~~	co10	2.58	-0.06	-0.06	-0.08	-0.08
pr07	~~	ut02	2.58	-0.04	-0.04	-0.09	-0.09
pr02	~~	pr08	2.57	-0.04	-0.04	-0.08	-0.08
co05	~~	ut06	2.57	0.04	0.04	0.08	0.08
co09	~~	co10	2.56	-0.05	-0.05	-0.08	-0.08
pr01	~~	fa08	2.55	-0.05	-0.05	-0.08	-0.08
un11	~~	un12	2.55	0.05	0.05	0.08	0.08
co08	~~	un12	2.54	-0.06	-0.06	-0.08	-0.08
pr06	~~	de10	2.53	-0.06	-0.06	-0.08	-0.08
co09	~~	un08	2.53	-0.04	-0.04	-0.08	-0.08
un08	~~	un12	2.52	0.05	0.05	0.08	0.08
ut02	~~	de01	2.51	-0.04	-0.04	-0.08	-0.08
fa04	~~	un02	2.50	0.05	0.05	0.08	0.08
pr02	~~	ut09	2.50	-0.05	-0.05	-0.08	-0.08
fa09	~~	de08	2.49	-0.06	-0.06	-0.08	-0.08
ut02	~~	ut06	2.48	-0.04	-0.04	-0.09	-0.09
pr07	~~	co04	2.48	-0.06	-0.06	-0.08	-0.08
co05	~~	un08	2.48	-0.04	-0.04	-0.08	-0.08
ut12	~~	de06	2.47	-0.06	-0.06	-0.08	-0.08
co10	~~	ut06	2.46	-0.05	-0.05	-0.08	-0.08
ut06	~~	ut07	2.46	0.05	0.05	0.08	0.08
co02	~~	un07	2.45	0.05	0.05	0.08	0.08
co09	~~	ut12	2.45	-0.05	-0.05	-0.08	-0.08
co04	~~	un03	2.45	0.07	0.07	0.07	0.07
fa01	~~	de01	2.44	-0.05	-0.05	-0.08	-0.08
co03	~~	ut12	2.43	-0.05	-0.05	-0.07	-0.07
pr10	~~	ut07	2.43	0.06	0.06	0.07	0.07
co05	~~	de10	2.42	0.05	0.05	0.08	0.08
ut02	~~	de02	2.42	-0.04	-0.04	-0.08	-0.08
fa09	~~	un01	2.42	-0.05	-0.05	-0.07	-0.07
pr06	~~	fa05	2.41	0.04	0.04	0.09	0.09
fa10	~~	un05	2.41	0.05	0.05	0.08	0.08
un03	~~	un11	2.41	-0.06	-0.06	-0.08	-0.08
fa05	~~	un08	2.40	-0.04	-0.04	-0.09	-0.09
co08	~~	ut07	2.40	0.07	0.07	0.07	0.07
un01	~~	un09	2.39	-0.05	-0.05	-0.08	-0.08
fa01	~~	de10	2.38	-0.04	-0.04	-0.09	-0.09
fa02	~~	de05	2.38	-0.08	-0.08	-0.07	-0.07
co01	~~	ut11	2.38	-0.06	-0.06	-0.08	-0.08
pr10	~~	fa06	2.38	0.06	0.06	0.07	0.07
co02	~~	co06	2.38	0.05	0.05	0.08	0.08
fa08	~~	un08	2.37	0.06	0.06	0.07	0.07
ut02	~~	un06	2.36	0.05	0.05	0.08	0.08
de09	~~	un03	2.36	0.08	0.08	0.07	0.07
un02	~~	un11	2.35	-0.04	-0.04	-0.08	-0.08
fa02	~~	de02	2.33	-0.06	-0.06	-0.07	-0.07
pr10	~~	ut03	2.33	-0.06	-0.06	-0.07	-0.07
co01	~~	de06	2.32	-0.05	-0.05	-0.08	-0.08
co01	~~	un11	2.32	0.04	0.04	0.08	0.08
pr06	~~	un03	2.29	0.06	0.06	0.07	0.07
pr09	~~	un09	2.28	-0.05	-0.05	-0.07	-0.07
co10	~~	de08	2.27	-0.05	-0.05	-0.07	-0.07
co08	~~	de02	2.26	0.06	0.06	0.07	0.07
co04	~~	un02	2.26	-0.05	-0.05	-0.07	-0.07
ut08	~~	un12	2.26	-0.05	-0.05	-0.07	-0.07
un05	~~	un08	2.25	0.04	0.04	0.08	0.08
fa01	~~	un08	2.25	0.04	0.04	0.08	0.08
ut01	~~	ut07	2.24	-0.05	-0.05	-0.08	-0.08
ut06	~~	ut08	2.23	0.04	0.04	0.07	0.07
co09	~~	un11	2.22	0.04	0.04	0.08	0.08
pr01	~~	de01	2.21	0.04	0.04	0.07	0.07
pr10	~~	ut01	2.21	0.04	0.04	0.07	0.07
pr06	~~	un09	2.21	0.05	0.05	0.07	0.07
pr05	~~	ut05	2.20	0.06	0.06	0.07	0.07
pr10	~~	un10	2.20	-0.05	-0.05	-0.07	-0.07
ut07	~~	de02	2.19	0.05	0.05	0.07	0.07
fa10	~~	un03	2.19	-0.07	-0.07	-0.07	-0.07
de06	~~	de09	2.18	-0.08	-0.08	-0.07	-0.07
pr01	~~	de02	2.18	-0.04	-0.04	-0.08	-0.08
ut12	~~	fa02	2.18	-0.06	-0.06	-0.07	-0.07
ut07	~~	de08	2.16	0.05	0.05	0.07	0.07
pr09	~~	un04	2.15	-0.04	-0.04	-0.07	-0.07
pr07	~~	un03	2.15	-0.05	-0.05	-0.07	-0.07
pr01	~~	un10	2.14	-0.04	-0.04	-0.07	-0.07
pr05	~~	fa10	2.14	-0.07	-0.07	-0.07	-0.07
co01	~~	fa04	2.12	0.05	0.05	0.07	0.07
un03	~~	un07	2.12	-0.06	-0.06	-0.07	-0.07
fa01	~~	un06	2.12	-0.05	-0.05	-0.08	-0.08
pr09	~~	ut02	2.11	-0.03	-0.03	-0.07	-0.07
co06	~~	un08	2.10	-0.05	-0.05	-0.07	-0.07
pr01	~~	ut05	2.10	-0.04	-0.04	-0.07	-0.07
co05	~~	un10	2.10	0.04	0.04	0.07	0.07
pr06	~~	fa02	2.09	-0.06	-0.06	-0.07	-0.07
ut05	~~	un01	2.07	-0.04	-0.04	-0.07	-0.07
pr06	~~	un12	2.07	0.05	0.05	0.07	0.07
ut12	~~	fa10	2.07	-0.06	-0.06	-0.07	-0.07
ut01	~~	ut12	2.07	-0.04	-0.04	-0.07	-0.07
fa02	~~	de08	2.06	-0.06	-0.06	-0.07	-0.07
ut07	~~	fa02	2.04	-0.07	-0.07	-0.07	-0.07
pr08	~~	fa05	2.04	-0.03	-0.03	-0.08	-0.08
ut02	~~	ut11	2.03	-0.05	-0.05	-0.07	-0.07
de08	~~	un07	2.03	0.05	0.05	0.07	0.07
ut04	~~	de10	2.03	0.06	0.06	0.07	0.07
fa08	~~	de07	2.03	-0.06	-0.06	-0.07	-0.07
ut08	~~	fa02	2.02	-0.06	-0.06	-0.07	-0.07
pr05	~~	co03	2.01	0.06	0.06	0.07	0.07
pr06	~~	ut01	2.01	0.04	0.04	0.07	0.07
fa05	~~	fa06	2.01	0.05	0.05	0.09	0.09
co03	~~	un05	2.00	0.04	0.04	0.07	0.07
pr01	~~	de07	2.00	-0.03	-0.03	-0.07	-0.07
pr02	~~	un08	2.00	0.04	0.04	0.07	0.07
co02	~~	ut04	2.00	0.05	0.05	0.07	0.07
pr07	~~	ut06	2.00	-0.04	-0.04	-0.07	-0.07
pr08	~~	fa09	1.99	-0.04	-0.04	-0.07	-0.07
pr07	~~	co02	1.99	-0.04	-0.04	-0.07	-0.07
ut04	~~	de02	1.98	-0.05	-0.05	-0.07	-0.07
pr01	~~	un07	1.98	-0.04	-0.04	-0.07	-0.07
pr08	~~	fa04	1.98	-0.04	-0.04	-0.07	-0.07
ut08	~~	de03	1.98	0.05	0.05	0.07	0.07
co05	~~	ut02	1.97	-0.03	-0.03	-0.08	-0.08
un01	~~	un03	1.96	-0.05	-0.05	-0.07	-0.07
de06	~~	un02	1.96	0.04	0.04	0.07	0.07
co06	~~	de09	1.95	0.07	0.07	0.07	0.07
un01	~~	un06	1.94	-0.05	-0.05	-0.07	-0.07
ut03	~~	de02	1.94	-0.05	-0.05	-0.07	-0.07
de09	~~	un08	1.93	0.06	0.06	0.07	0.07
de02	~~	un02	1.93	0.04	0.04	0.07	0.07
ut09	~~	un05	1.93	-0.04	-0.04	-0.07	-0.07
ut05	~~	de09	1.92	-0.07	-0.07	-0.06	-0.06
ut05	~~	ut11	1.92	-0.06	-0.06	-0.07	-0.07
de01	~~	un05	1.92	-0.04	-0.04	-0.07	-0.07
pr01	~~	un02	1.91	-0.03	-0.03	-0.07	-0.07
ut07	~~	fa01	1.91	0.04	0.04	0.07	0.07
pr08	~~	fa10	1.91	0.04	0.04	0.07	0.07
ut05	~~	un05	1.90	-0.04	-0.04	-0.07	-0.07
ut01	~~	ut08	1.90	-0.04	-0.04	-0.07	-0.07
co05	~~	ut11	1.90	0.05	0.05	0.07	0.07
pr01	~~	un12	1.89	0.03	0.03	0.07	0.07
co08	~~	fa02	1.89	-0.07	-0.07	-0.06	-0.06
de07	~~	un03	1.88	0.05	0.05	0.06	0.06
pr02	~~	un02	1.87	0.03	0.03	0.07	0.07
fa09	~~	un08	1.86	0.05	0.05	0.07	0.07
fa04	~~	un04	1.86	0.05	0.05	0.06	0.06
fa08	~~	un11	1.85	-0.05	-0.05	-0.06	-0.06
ut02	~~	de10	1.84	-0.03	-0.03	-0.07	-0.07
pr10	~~	fa01	1.82	-0.04	-0.04	-0.07	-0.07
ut02	~~	fa09	1.82	0.04	0.04	0.07	0.07
pr08	~~	pr09	1.80	0.03	0.03	0.07	0.07
co05	~~	un02	1.80	0.03	0.03	0.07	0.07
de03	~~	un09	1.79	-0.05	-0.05	-0.06	-0.06
co05	~~	de07	1.79	-0.04	-0.04	-0.07	-0.07
co10	~~	fa04	1.77	0.06	0.06	0.06	0.06
ut06	~~	un12	1.76	-0.04	-0.04	-0.07	-0.07
fa08	~~	de01	1.76	-0.06	-0.06	-0.06	-0.06
pr10	~~	un11	1.76	0.04	0.04	0.06	0.06
co05	~~	de09	1.75	-0.05	-0.05	-0.07	-0.07
co02	~~	co09	1.75	-0.04	-0.04	-0.07	-0.07
fa02	~~	de07	1.75	-0.05	-0.05	-0.06	-0.06
ut04	~~	un09	1.75	0.05	0.05	0.06	0.06
pr06	~~	co02	1.75	-0.04	-0.04	-0.06	-0.06
ut05	~~	de05	1.74	-0.06	-0.06	-0.06	-0.06
co03	~~	un04	1.74	-0.04	-0.04	-0.06	-0.06
ut01	~~	de07	1.74	0.03	0.03	0.07	0.07
co02	~~	de02	1.73	0.04	0.04	0.07	0.07
pr05	~~	fa06	1.72	0.05	0.05	0.06	0.06
ut04	~~	fa05	1.72	0.04	0.04	0.07	0.07
de10	~~	un11	1.72	0.04	0.04	0.07	0.07
de02	~~	un08	1.72	-0.04	-0.04	-0.06	-0.06
co10	~~	de03	1.72	0.05	0.05	0.06	0.06
pr10	~~	co05	1.71	-0.04	-0.04	-0.07	-0.07
co01	~~	ut01	1.71	-0.03	-0.03	-0.07	-0.07
pr09	~~	co09	1.71	-0.04	-0.04	-0.06	-0.06
co10	~~	ut02	1.69	0.03	0.03	0.07	0.07
de08	~~	un12	1.68	0.04	0.04	0.06	0.06
pr05	~~	un11	1.68	0.05	0.05	0.06	0.06
pr01	~~	de06	1.68	-0.04	-0.04	-0.07	-0.07
co01	~~	de09	1.68	-0.05	-0.05	-0.06	-0.06
ut09	~~	un03	1.68	-0.05	-0.05	-0.06	-0.06
ut07	~~	de09	1.67	-0.06	-0.06	-0.06	-0.06
pr08	~~	ut04	1.66	-0.04	-0.04	-0.06	-0.06
pr08	~~	co10	1.65	-0.03	-0.03	-0.06	-0.06
pr05	~~	un02	1.65	-0.04	-0.04	-0.06	-0.06
ut01	~~	de09	1.65	0.05	0.05	0.06	0.06
ut09	~~	de06	1.64	-0.05	-0.05	-0.06	-0.06
co04	~~	un11	1.63	-0.05	-0.05	-0.06	-0.06
co06	~~	de07	1.63	0.04	0.04	0.06	0.06
fa08	~~	un07	1.63	-0.05	-0.05	-0.06	-0.06
ut04	~~	un02	1.62	-0.04	-0.04	-0.06	-0.06
pr10	~~	un02	1.62	0.04	0.04	0.06	0.06
de06	~~	un04	1.62	0.04	0.04	0.06	0.06
pr09	~~	ut09	1.61	-0.04	-0.04	-0.06	-0.06
pr08	~~	un11	1.61	0.03	0.03	0.06	0.06
ut12	~~	de10	1.61	0.04	0.04	0.06	0.06
co02	~~	de10	1.61	-0.04	-0.04	-0.06	-0.06
ut12	~~	fa01	1.61	-0.04	-0.04	-0.07	-0.07
co05	~~	un09	1.61	-0.04	-0.04	-0.07	-0.07
pr07	~~	pr08	1.60	0.03	0.03	0.07	0.07
ut03	~~	de06	1.59	-0.05	-0.05	-0.06	-0.06
ut09	~~	un06	1.59	-0.06	-0.06	-0.06	-0.06
de06	~~	de07	1.59	-0.05	-0.05	-0.06	-0.06
ut08	~~	un07	1.58	-0.04	-0.04	-0.06	-0.06
co06	~~	un11	1.57	-0.04	-0.04	-0.06	-0.06
co01	~~	fa06	1.57	0.04	0.04	0.06	0.06
fa06	~~	un06	1.57	-0.05	-0.05	-0.06	-0.06
ut03	~~	un03	1.57	0.05	0.05	0.06	0.06
pr07	~~	fa04	1.57	0.05	0.05	0.06	0.06
ut06	~~	un06	1.56	-0.05	-0.05	-0.06	-0.06
pr09	~~	de07	1.56	-0.04	-0.04	-0.06	-0.06
un04	~~	un11	1.53	-0.04	-0.04	-0.06	-0.06
fa04	~~	de01	1.53	0.05	0.05	0.06	0.06
co08	~~	de07	1.53	-0.04	-0.04	-0.06	-0.06
de09	~~	un02	1.52	-0.04	-0.04	-0.06	-0.06
de10	~~	un02	1.52	-0.03	-0.03	-0.06	-0.06
ut04	~~	un12	1.51	0.04	0.04	0.06	0.06
ut06	~~	un03	1.51	0.04	0.04	0.06	0.06
ut04	~~	ut08	1.50	-0.05	-0.05	-0.06	-0.06
co09	~~	fa01	1.50	-0.03	-0.03	-0.07	-0.07
ut06	~~	de05	1.50	0.04	0.04	0.06	0.06
pr02	~~	de03	1.50	0.04	0.04	0.06	0.06
pr05	~~	de09	1.49	-0.06	-0.06	-0.06	-0.06
ut06	~~	fa08	1.49	-0.04	-0.04	-0.06	-0.06
ut05	~~	de03	1.49	-0.05	-0.05	-0.06	-0.06
pr09	~~	de05	1.48	0.04	0.04	0.06	0.06
ut03	~~	un11	1.48	-0.04	-0.04	-0.06	-0.06
pr01	~~	co02	1.46	0.03	0.03	0.06	0.06
fa06	~~	de10	1.46	0.04	0.04	0.06	0.06
pr02	~~	un09	1.46	-0.04	-0.04	-0.06	-0.06
pr08	~~	un06	1.45	-0.04	-0.04	-0.06	-0.06
ut01	~~	ut06	1.45	-0.03	-0.03	-0.06	-0.06
fa05	~~	un04	1.44	-0.03	-0.03	-0.07	-0.07
pr10	~~	de05	1.44	0.05	0.05	0.06	0.06
co09	~~	un09	1.44	0.04	0.04	0.06	0.06
un03	~~	un12	1.43	-0.04	-0.04	-0.06	-0.06
co05	~~	un03	1.41	-0.04	-0.04	-0.06	-0.06
fa02	~~	un04	1.40	-0.04	-0.04	-0.06	-0.06
ut03	~~	ut11	1.39	-0.06	-0.06	-0.05	-0.05
fa06	~~	un02	1.39	0.03	0.03	0.06	0.06
fa09	~~	un12	1.39	0.04	0.04	0.06	0.06
co10	~~	ut08	1.39	-0.04	-0.04	-0.06	-0.06
pr07	~~	co03	1.39	0.04	0.04	0.06	0.06
ut11	~~	un07	1.39	0.05	0.05	0.06	0.06
co01	~~	de07	1.38	-0.03	-0.03	-0.06	-0.06
ut06	~~	de01	1.38	-0.04	-0.04	-0.06	-0.06
pr06	~~	de09	1.38	-0.05	-0.05	-0.05	-0.05
un09	~~	un10	1.37	0.04	0.04	0.06	0.06
ut03	~~	ut09	1.37	-0.05	-0.05	-0.06	-0.06
pr10	~~	fa08	1.37	-0.05	-0.05	-0.05	-0.05
co01	~~	un02	1.37	-0.03	-0.03	-0.06	-0.06
co06	~~	ut01	1.37	0.03	0.03	0.06	0.06
ut03	~~	un01	1.36	0.04	0.04	0.06	0.06
co05	~~	fa09	1.36	0.04	0.04	0.06	0.06
fa10	~~	un12	1.35	0.04	0.04	0.06	0.06
ut06	~~	ut09	1.34	0.04	0.04	0.06	0.06
de06	~~	un03	1.34	-0.05	-0.05	-0.05	-0.05
de03	~~	un07	1.34	0.04	0.04	0.06	0.06
ut04	~~	un04	1.34	0.04	0.04	0.05	0.05
fa01	~~	un11	1.33	0.03	0.03	0.06	0.06
co05	~~	ut04	1.33	-0.04	-0.04	-0.06	-0.06
co09	~~	de08	1.33	-0.03	-0.03	-0.06	-0.06
un07	~~	un11	1.33	0.04	0.04	0.06	0.06
ut04	~~	fa06	1.32	-0.05	-0.05	-0.05	-0.05
ut07	~~	de10	1.32	-0.04	-0.04	-0.06	-0.06
de02	~~	un04	1.31	-0.03	-0.03	-0.06	-0.06
co02	~~	co08	1.31	-0.04	-0.04	-0.06	-0.06
un05	~~	un06	1.30	-0.04	-0.04	-0.06	-0.06
co04	~~	de01	1.30	-0.05	-0.05	-0.05	-0.05
pr08	~~	ut03	1.30	0.03	0.03	0.05	0.05
co03	~~	un02	1.29	-0.03	-0.03	-0.06	-0.06
ut08	~~	fa04	1.28	0.04	0.04	0.05	0.05
pr08	~~	un12	1.28	0.03	0.03	0.06	0.06
de09	~~	un04	1.27	-0.04	-0.04	-0.05	-0.05
ut02	~~	un11	1.27	-0.03	-0.03	-0.06	-0.06
ut05	~~	un04	1.27	-0.04	-0.04	-0.05	-0.05
pr07	~~	de01	1.25	-0.04	-0.04	-0.05	-0.05
co09	~~	ut05	1.25	0.04	0.04	0.06	0.06
pr07	~~	un11	1.25	0.03	0.03	0.06	0.06
fa09	~~	un04	1.24	0.04	0.04	0.05	0.05
co02	~~	co05	1.23	-0.03	-0.03	-0.06	-0.06
co02	~~	co10	1.23	0.04	0.04	0.06	0.06
ut04	~~	de03	1.23	0.05	0.05	0.05	0.05
pr07	~~	ut08	1.23	0.03	0.03	0.05	0.05
ut11	~~	un12	1.22	-0.04	-0.04	-0.05	-0.05
pr09	~~	co10	1.22	-0.04	-0.04	-0.05	-0.05
de07	~~	un01	1.22	0.03	0.03	0.05	0.05
de07	~~	un12	1.22	0.03	0.03	0.05	0.05
co03	~~	fa06	1.22	0.04	0.04	0.05	0.05
co01	~~	ut07	1.21	-0.04	-0.04	-0.05	-0.05
ut09	~~	fa08	1.21	-0.05	-0.05	-0.05	-0.05
un03	~~	un10	1.21	0.04	0.04	0.05	0.05
fa09	~~	de03	1.21	-0.05	-0.05	-0.05	-0.05
co04	~~	de06	1.20	-0.05	-0.05	-0.05	-0.05
co04	~~	de03	1.20	-0.05	-0.05	-0.05	-0.05
ut05	~~	fa06	1.20	0.04	0.04	0.05	0.05
co04	~~	ut11	1.20	-0.05	-0.05	-0.05	-0.05
pr01	~~	co10	1.20	0.03	0.03	0.06	0.06
pr07	~~	un10	1.20	0.03	0.03	0.05	0.05
ut07	~~	fa05	1.19	-0.03	-0.03	-0.06	-0.06
ut02	~~	fa01	1.18	0.02	0.02	0.06	0.06
co04	~~	fa09	1.18	-0.05	-0.05	-0.05	-0.05
co06	~~	ut08	1.18	0.04	0.04	0.05	0.05
ut01	~~	fa09	1.17	-0.04	-0.04	-0.05	-0.05
fa05	~~	fa10	1.15	-0.04	-0.04	-0.07	-0.07
co01	~~	un09	1.15	0.03	0.03	0.05	0.05
co02	~~	de08	1.14	-0.03	-0.03	-0.05	-0.05
co03	~~	de10	1.14	-0.04	-0.04	-0.05	-0.05
co02	~~	fa06	1.14	0.03	0.03	0.05	0.05
pr06	~~	ut08	1.14	-0.04	-0.04	-0.05	-0.05
pr08	~~	co09	1.14	-0.03	-0.03	-0.05	-0.05
fa08	~~	un02	1.14	-0.03	-0.03	-0.05	-0.05
pr02	~~	un07	1.14	-0.03	-0.03	-0.05	-0.05
fa05	~~	un03	1.14	0.03	0.03	0.06	0.06
pr05	~~	pr06	1.13	-0.04	-0.04	-0.05	-0.05
fa10	~~	de09	1.13	0.05	0.05	0.05	0.05
co02	~~	ut03	1.12	-0.04	-0.04	-0.05	-0.05
co05	~~	un12	1.12	-0.03	-0.03	-0.05	-0.05
ut07	~~	un03	1.12	-0.04	-0.04	-0.05	-0.05
pr02	~~	pr07	1.11	-0.03	-0.03	-0.05	-0.05
pr06	~~	de06	1.11	-0.04	-0.04	-0.05	-0.05
ut08	~~	un09	1.10	-0.04	-0.04	-0.05	-0.05
ut09	~~	un02	1.10	-0.03	-0.03	-0.05	-0.05
de07	~~	de09	1.09	-0.04	-0.04	-0.05	-0.05
ut11	~~	un04	1.09	0.04	0.04	0.05	0.05
ut09	~~	fa02	1.09	0.05	0.05	0.05	0.05
ut05	~~	de08	1.09	-0.04	-0.04	-0.05	-0.05
co01	~~	un08	1.09	-0.03	-0.03	-0.05	-0.05
co09	~~	fa02	1.09	0.04	0.04	0.05	0.05
pr05	~~	un03	1.08	-0.05	-0.05	-0.05	-0.05
ut07	~~	un04	1.07	0.03	0.03	0.05	0.05
pr07	~~	un02	1.07	0.03	0.03	0.05	0.05
ut08	~~	de06	1.07	-0.04	-0.04	-0.05	-0.05
pr06	~~	un05	1.07	0.03	0.03	0.05	0.05
pr09	~~	fa01	1.07	0.03	0.03	0.06	0.06
pr10	~~	de10	1.06	-0.04	-0.04	-0.05	-0.05
fa08	~~	un09	1.06	-0.05	-0.05	-0.05	-0.05
pr07	~~	un04	1.05	-0.03	-0.03	-0.05	-0.05
fa01	~~	de03	1.05	-0.03	-0.03	-0.06	-0.06
pr07	~~	fa02	1.04	-0.04	-0.04	-0.05	-0.05
pr01	~~	co03	1.04	-0.03	-0.03	-0.05	-0.05
co01	~~	co05	1.04	0.03	0.03	0.06	0.06
co10	~~	un02	1.04	0.03	0.03	0.05	0.05
pr02	~~	fa10	1.04	-0.04	-0.04	-0.05	-0.05
fa09	~~	un09	1.03	-0.04	-0.04	-0.05	-0.05
co10	~~	un12	1.02	0.03	0.03	0.05	0.05
ut05	~~	fa10	1.02	0.04	0.04	0.05	0.05
pr08	~~	un09	1.01	-0.03	-0.03	-0.05	-0.05
fa02	~~	un01	1.00	-0.04	-0.04	-0.05	-0.05
pr09	~~	un10	1.00	-0.03	-0.03	-0.05	-0.05
co01	~~	un12	1.00	-0.03	-0.03	-0.05	-0.05
co05	~~	co06	0.97	0.03	0.03	0.05	0.05
co04	~~	fa01	0.97	0.03	0.03	0.05	0.05
pr06	~~	co10	0.97	0.04	0.04	0.05	0.05
ut09	~~	un04	0.96	-0.03	-0.03	-0.05	-0.05
pr06	~~	un04	0.96	0.03	0.03	0.05	0.05
co04	~~	co10	0.96	0.04	0.04	0.05	0.05
co01	~~	un04	0.96	-0.03	-0.03	-0.05	-0.05
co08	~~	fa01	0.96	-0.03	-0.03	-0.05	-0.05
pr10	~~	un03	0.95	0.04	0.04	0.05	0.05
ut08	~~	un02	0.94	0.03	0.03	0.05	0.05
un05	~~	un11	0.94	-0.03	-0.03	-0.05	-0.05
pr02	~~	un10	0.94	0.03	0.03	0.05	0.05
pr07	~~	de03	0.94	0.03	0.03	0.05	0.05
ut09	~~	ut12	0.93	0.03	0.03	0.05	0.05
co06	~~	un05	0.93	-0.03	-0.03	-0.05	-0.05
pr01	~~	de09	0.93	0.03	0.03	0.05	0.05
de08	~~	un11	0.92	0.03	0.03	0.05	0.05
ut11	~~	un05	0.91	-0.03	-0.03	-0.05	-0.05
de01	~~	un07	0.91	0.04	0.04	0.05	0.05
co01	~~	fa09	0.91	-0.04	-0.04	-0.05	-0.05
ut12	~~	un04	0.91	0.03	0.03	0.05	0.05
co01	~~	fa02	0.91	0.04	0.04	0.05	0.05
ut06	~~	un07	0.90	0.03	0.03	0.05	0.05
de08	~~	de10	0.89	0.03	0.03	0.05	0.05
de08	~~	un09	0.89	0.03	0.03	0.05	0.05
co02	~~	co04	0.88	0.04	0.04	0.05	0.05
ut09	~~	de08	0.87	0.03	0.03	0.05	0.05
ut06	~~	fa04	0.87	0.03	0.03	0.04	0.04
co03	~~	de01	0.86	-0.03	-0.03	-0.04	-0.04
pr02	~~	fa05	0.86	-0.02	-0.02	-0.05	-0.05
pr10	~~	un12	0.86	-0.03	-0.03	-0.04	-0.04
pr10	~~	fa05	0.86	-0.03	-0.03	-0.05	-0.05
co10	~~	fa06	0.86	0.03	0.03	0.04	0.04
ut05	~~	ut12	0.85	0.03	0.03	0.05	0.05
pr10	~~	ut09	0.85	-0.03	-0.03	-0.04	-0.04
pr10	~~	ut06	0.85	0.03	0.03	0.04	0.04
pr10	~~	de01	0.85	0.04	0.04	0.04	0.04
ut02	~~	ut07	0.85	-0.03	-0.03	-0.05	-0.05
ut11	~~	un08	0.85	-0.04	-0.04	-0.04	-0.04
de05	~~	de07	0.84	0.03	0.03	0.04	0.04
co02	~~	un02	0.84	0.02	0.02	0.05	0.05
ut08	~~	un01	0.83	0.03	0.03	0.04	0.04
co09	~~	ut06	0.83	-0.02	-0.02	-0.05	-0.05
co01	~~	un03	0.83	-0.03	-0.03	-0.04	-0.04
ut12	~~	fa09	0.83	-0.04	-0.04	-0.04	-0.04
pr05	~~	un08	0.83	-0.03	-0.03	-0.04	-0.04
de07	~~	un11	0.82	-0.03	-0.03	-0.04	-0.04
pr10	~~	un08	0.82	0.03	0.03	0.04	0.04
ut01	~~	un10	0.82	-0.02	-0.02	-0.05	-0.05
fa05	~~	de02	0.82	0.02	0.02	0.05	0.05
de10	~~	un04	0.81	0.03	0.03	0.04	0.04
co10	~~	de07	0.81	-0.03	-0.03	-0.04	-0.04
de07	~~	un02	0.81	-0.02	-0.02	-0.05	-0.05
un06	~~	un07	0.81	0.04	0.04	0.04	0.04
pr08	~~	ut05	0.81	-0.03	-0.03	-0.04	-0.04
co02	~~	fa05	0.81	-0.02	-0.02	-0.05	-0.05
ut11	~~	fa02	0.80	-0.05	-0.05	-0.04	-0.04
ut07	~~	de03	0.80	-0.04	-0.04	-0.04	-0.04
un04	~~	un10	0.80	-0.03	-0.03	-0.04	-0.04
co08	~~	de08	0.80	-0.03	-0.03	-0.04	-0.04
co04	~~	ut08	0.79	0.03	0.03	0.04	0.04
pr09	~~	fa02	0.79	-0.03	-0.03	-0.04	-0.04
pr01	~~	fa02	0.79	-0.03	-0.03	-0.04	-0.04
fa01	~~	de05	0.79	0.03	0.03	0.05	0.05
de10	~~	un12	0.79	-0.03	-0.03	-0.04	-0.04
pr01	~~	ut09	0.79	0.02	0.02	0.04	0.04
co06	~~	de10	0.78	-0.03	-0.03	-0.04	-0.04
pr08	~~	fa01	0.78	0.02	0.02	0.05	0.05
ut04	~~	de09	0.77	-0.04	-0.04	-0.04	-0.04
de01	~~	un04	0.77	-0.03	-0.03	-0.04	-0.04
pr06	~~	un01	0.76	-0.03	-0.03	-0.04	-0.04
fa04	~~	un11	0.76	0.03	0.03	0.04	0.04
de07	~~	de08	0.75	0.03	0.03	0.04	0.04
pr01	~~	fa01	0.75	0.02	0.02	0.05	0.05
co02	~~	ut06	0.75	-0.02	-0.02	-0.04	-0.04
de03	~~	un04	0.75	-0.03	-0.03	-0.04	-0.04
pr10	~~	co04	0.74	0.04	0.04	0.04	0.04
ut09	~~	un10	0.74	0.03	0.03	0.04	0.04
ut08	~~	un10	0.73	-0.03	-0.03	-0.04	-0.04
de03	~~	de08	0.73	-0.03	-0.03	-0.04	-0.04
ut11	~~	fa05	0.73	-0.03	-0.03	-0.05	-0.05
ut04	~~	de01	0.73	-0.04	-0.04	-0.04	-0.04
co09	~~	ut01	0.73	-0.02	-0.02	-0.04	-0.04
ut07	~~	de07	0.73	-0.03	-0.03	-0.04	-0.04
pr06	~~	fa08	0.72	-0.04	-0.04	-0.04	-0.04
pr02	~~	un12	0.72	0.02	0.02	0.04	0.04
pr07	~~	de08	0.71	-0.02	-0.02	-0.04	-0.04
co03	~~	fa10	0.71	0.03	0.03	0.04	0.04
pr06	~~	de05	0.71	0.03	0.03	0.04	0.04
pr05	~~	ut06	0.70	-0.03	-0.03	-0.04	-0.04
ut11	~~	de08	0.70	0.03	0.03	0.04	0.04
un01	~~	un02	0.69	-0.02	-0.02	-0.04	-0.04
pr07	~~	fa10	0.69	0.03	0.03	0.04	0.04
ut09	~~	fa01	0.69	0.02	0.02	0.05	0.05
ut05	~~	fa08	0.68	0.04	0.04	0.04	0.04
ut07	~~	de05	0.67	-0.04	-0.04	-0.04	-0.04
pr08	~~	un05	0.67	0.02	0.02	0.04	0.04
de02	~~	un06	0.67	0.03	0.03	0.04	0.04
fa01	~~	un12	0.66	-0.02	-0.02	-0.05	-0.05
co08	~~	fa06	0.66	-0.03	-0.03	-0.04	-0.04
co08	~~	fa10	0.66	-0.04	-0.04	-0.04	-0.04
un05	~~	un10	0.65	0.02	0.02	0.04	0.04
ut07	~~	un08	0.64	0.03	0.03	0.04	0.04
fa01	~~	fa02	0.64	-0.03	-0.03	-0.05	-0.05
de06	~~	de08	0.64	-0.03	-0.03	-0.04	-0.04
pr09	~~	fa05	0.64	-0.02	-0.02	-0.04	-0.04
co03	~~	ut02	0.63	0.02	0.02	0.04	0.04
fa10	~~	de07	0.63	-0.03	-0.03	-0.04	-0.04
ut01	~~	un11	0.62	-0.02	-0.02	-0.04	-0.04
pr08	~~	co06	0.62	-0.02	-0.02	-0.04	-0.04
ut11	~~	un10	0.62	0.03	0.03	0.04	0.04
ut08	~~	fa08	0.62	-0.03	-0.03	-0.04	-0.04
de08	~~	un08	0.62	-0.02	-0.02	-0.04	-0.04
pr01	~~	fa10	0.62	-0.02	-0.02	-0.04	-0.04
ut11	~~	un06	0.62	-0.04	-0.04	-0.04	-0.04
fa05	~~	un07	0.61	0.02	0.02	0.04	0.04
de05	~~	un09	0.61	-0.03	-0.03	-0.04	-0.04
de07	~~	un07	0.61	0.03	0.03	0.04	0.04
co05	~~	ut07	0.61	0.03	0.03	0.04	0.04
ut08	~~	fa09	0.61	-0.03	-0.03	-0.04	-0.04
pr10	~~	de03	0.60	-0.03	-0.03	-0.04	-0.04
pr09	~~	de09	0.60	0.03	0.03	0.04	0.04
pr10	~~	co03	0.60	0.03	0.03	0.04	0.04
fa01	~~	de02	0.59	-0.02	-0.02	-0.04	-0.04
pr09	~~	de06	0.59	-0.03	-0.03	-0.04	-0.04
pr02	~~	ut07	0.59	-0.03	-0.03	-0.04	-0.04
ut12	~~	un10	0.58	0.02	0.02	0.04	0.04
ut05	~~	un12	0.58	-0.03	-0.03	-0.04	-0.04
ut12	~~	un05	0.58	-0.02	-0.02	-0.04	-0.04
co02	~~	de03	0.58	0.03	0.03	0.04	0.04
pr01	~~	co06	0.58	-0.02	-0.02	-0.04	-0.04
co08	~~	un08	0.57	-0.03	-0.03	-0.04	-0.04
pr05	~~	pr07	0.57	-0.03	-0.03	-0.04	-0.04
pr05	~~	pr10	0.57	-0.03	-0.03	-0.04	-0.04
co05	~~	un05	0.57	-0.02	-0.02	-0.04	-0.04
co03	~~	fa09	0.57	-0.03	-0.03	-0.04	-0.04
ut08	~~	fa10	0.57	0.03	0.03	0.04	0.04
ut03	~~	ut05	0.57	-0.03	-0.03	-0.04	-0.04
ut05	~~	un02	0.56	-0.02	-0.02	-0.04	-0.04
fa10	~~	un04	0.56	0.03	0.03	0.04	0.04
co01	~~	fa10	0.56	-0.03	-0.03	-0.04	-0.04
co05	~~	fa04	0.55	0.03	0.03	0.04	0.04
ut07	~~	ut12	0.55	0.03	0.03	0.04	0.04
ut03	~~	ut06	0.54	-0.03	-0.03	-0.04	-0.04
de08	~~	un06	0.54	-0.03	-0.03	-0.04	-0.04
co10	~~	ut12	0.54	0.03	0.03	0.04	0.04
pr06	~~	ut03	0.53	-0.03	-0.03	-0.03	-0.03
pr05	~~	de02	0.53	0.03	0.03	0.04	0.04
pr08	~~	un04	0.53	-0.02	-0.02	-0.04	-0.04
co04	~~	ut09	0.53	0.03	0.03	0.03	0.03
fa05	~~	un11	0.52	-0.02	-0.02	-0.04	-0.04
ut02	~~	un09	0.52	0.02	0.02	0.04	0.04
fa10	~~	de06	0.52	-0.03	-0.03	-0.03	-0.03
de03	~~	un01	0.52	-0.02	-0.02	-0.03	-0.03
pr09	~~	un03	0.52	-0.03	-0.03	-0.03	-0.03
fa09	~~	de10	0.52	0.03	0.03	0.03	0.03
pr02	~~	co01	0.52	-0.02	-0.02	-0.04	-0.04
pr10	~~	ut02	0.52	0.02	0.02	0.04	0.04
pr02	~~	un01	0.51	-0.02	-0.02	-0.03	-0.03
co05	~~	un06	0.51	0.03	0.03	0.04	0.04
ut11	~~	un11	0.51	0.03	0.03	0.03	0.03
ut09	~~	un08	0.51	0.02	0.02	0.03	0.03
pr09	~~	un12	0.51	-0.02	-0.02	-0.03	-0.03
pr01	~~	pr02	0.51	0.02	0.02	0.04	0.04
un03	~~	un09	0.51	0.03	0.03	0.03	0.03
co01	~~	ut05	0.50	0.02	0.02	0.04	0.04
pr01	~~	un06	0.50	-0.02	-0.02	-0.03	-0.03
pr01	~~	pr08	0.50	0.01	0.01	0.04	0.04
pr09	~~	co05	0.50	-0.02	-0.02	-0.04	-0.04
ut04	~~	un05	0.50	-0.02	-0.02	-0.03	-0.03
pr05	~~	un12	0.50	0.03	0.03	0.03	0.03
ut12	~~	un11	0.50	0.02	0.02	0.03	0.03
co04	~~	fa05	0.49	0.02	0.02	0.04	0.04
ut09	~~	un11	0.49	0.02	0.02	0.03	0.03
ut09	~~	de09	0.49	-0.03	-0.03	-0.03	-0.03
co03	~~	ut08	0.49	-0.02	-0.02	-0.03	-0.03
co05	~~	de02	0.48	0.02	0.02	0.04	0.04
pr10	~~	un04	0.48	-0.02	-0.02	-0.03	-0.03
co04	~~	ut12	0.48	-0.03	-0.03	-0.03	-0.03
co10	~~	de06	0.47	0.03	0.03	0.03	0.03
ut05	~~	fa01	0.47	-0.02	-0.02	-0.04	-0.04
un04	~~	un07	0.47	-0.02	-0.02	-0.03	-0.03
de02	~~	de08	0.47	0.02	0.02	0.04	0.04
pr06	~~	fa04	0.47	-0.03	-0.03	-0.03	-0.03
pr07	~~	fa08	0.46	0.03	0.03	0.03	0.03
co04	~~	de08	0.46	0.03	0.03	0.03	0.03
pr08	~~	fa06	0.46	0.02	0.02	0.03	0.03
co05	~~	fa01	0.45	-0.02	-0.02	-0.04	-0.04
pr08	~~	de10	0.45	0.02	0.02	0.03	0.03
pr02	~~	pr06	0.45	0.02	0.02	0.03	0.03
pr07	~~	ut12	0.45	0.02	0.02	0.03	0.03
ut07	~~	fa09	0.45	-0.03	-0.03	-0.03	-0.03
fa01	~~	fa04	0.44	0.02	0.02	0.04	0.04
co04	~~	ut07	0.44	-0.03	-0.03	-0.03	-0.03
ut08	~~	un11	0.44	-0.02	-0.02	-0.03	-0.03
de07	~~	un04	0.44	0.02	0.02	0.03	0.03
co01	~~	un06	0.44	0.03	0.03	0.03	0.03
un03	~~	un06	0.44	-0.03	-0.03	-0.03	-0.03
fa04	~~	un03	0.43	0.03	0.03	0.03	0.03
co03	~~	ut07	0.43	0.03	0.03	0.03	0.03
ut01	~~	un04	0.43	0.02	0.02	0.03	0.03
pr10	~~	de08	0.42	-0.02	-0.02	-0.03	-0.03
ut05	~~	de07	0.42	0.02	0.02	0.03	0.03
de03	~~	un03	0.42	0.03	0.03	0.03	0.03
co08	~~	un09	0.42	0.03	0.03	0.03	0.03
pr02	~~	ut02	0.42	-0.02	-0.02	-0.03	-0.03
co10	~~	ut07	0.41	0.02	0.02	0.03	0.03
ut12	~~	fa08	0.41	0.03	0.03	0.03	0.03
co03	~~	un07	0.41	-0.02	-0.02	-0.03	-0.03
ut12	~~	un07	0.41	0.02	0.02	0.03	0.03
co01	~~	un10	0.41	-0.02	-0.02	-0.03	-0.03
co06	~~	ut03	0.41	-0.03	-0.03	-0.03	-0.03
co01	~~	ut03	0.40	0.02	0.02	0.03	0.03
co06	~~	un02	0.40	0.02	0.02	0.03	0.03
pr06	~~	co06	0.40	0.02	0.02	0.03	0.03
pr09	~~	ut01	0.40	-0.02	-0.02	-0.03	-0.03
pr08	~~	ut07	0.39	-0.02	-0.02	-0.03	-0.03
co03	~~	ut06	0.39	0.02	0.02	0.03	0.03
ut06	~~	fa05	0.39	0.02	0.02	0.04	0.04
de03	~~	un05	0.39	-0.02	-0.02	-0.03	-0.03
un01	~~	un04	0.39	-0.02	-0.02	-0.03	-0.03
pr02	~~	de09	0.39	0.03	0.03	0.03	0.03
co02	~~	ut01	0.39	0.02	0.02	0.03	0.03
co09	~~	un04	0.39	0.02	0.02	0.03	0.03
ut06	~~	un09	0.38	-0.02	-0.02	-0.03	-0.03
co01	~~	ut09	0.38	0.02	0.02	0.03	0.03
ut09	~~	fa06	0.38	-0.02	-0.02	-0.03	-0.03
pr01	~~	pr06	0.38	0.02	0.02	0.03	0.03
un10	~~	un12	0.38	-0.02	-0.02	-0.03	-0.03
de06	~~	un12	0.37	-0.02	-0.02	-0.03	-0.03
pr09	~~	fa10	0.37	-0.02	-0.02	-0.03	-0.03
fa01	~~	fa09	0.37	0.02	0.02	0.04	0.04
pr08	~~	un03	0.37	0.02	0.02	0.03	0.03
ut08	~~	de08	0.37	-0.02	-0.02	-0.03	-0.03
pr02	~~	pr09	0.37	-0.02	-0.02	-0.03	-0.03
de07	~~	un05	0.37	0.02	0.02	0.03	0.03
ut06	~~	de10	0.36	-0.02	-0.02	-0.03	-0.03
co08	~~	ut08	0.36	-0.02	-0.02	-0.03	-0.03
un10	~~	un11	0.36	0.02	0.02	0.03	0.03
fa05	~~	de07	0.36	-0.02	-0.02	-0.03	-0.03
pr08	~~	co01	0.36	-0.01	-0.01	-0.03	-0.03
ut06	~~	un10	0.36	-0.02	-0.02	-0.03	-0.03
co09	~~	un10	0.36	0.02	0.02	0.03	0.03
co03	~~	de06	0.35	-0.02	-0.02	-0.03	-0.03
fa10	~~	un11	0.35	-0.02	-0.02	-0.03	-0.03
ut09	~~	un01	0.35	0.02	0.02	0.03	0.03
fa05	~~	un02	0.35	-0.01	-0.01	-0.03	-0.03
co01	~~	co02	0.35	-0.02	-0.02	-0.03	-0.03
fa06	~~	un12	0.35	-0.02	-0.02	-0.03	-0.03
pr10	~~	co01	0.35	-0.02	-0.02	-0.03	-0.03
ut08	~~	un03	0.35	0.02	0.02	0.03	0.03
de01	~~	un11	0.34	0.02	0.02	0.03	0.03
pr01	~~	ut04	0.34	-0.02	-0.02	-0.03	-0.03
fa06	~~	de07	0.34	0.02	0.02	0.03	0.03
pr10	~~	fa10	0.34	-0.02	-0.02	-0.03	-0.03
fa10	~~	de03	0.33	-0.03	-0.03	-0.03	-0.03
co03	~~	fa08	0.33	0.02	0.02	0.03	0.03
pr09	~~	un02	0.33	0.01	0.01	0.03	0.03
un04	~~	un05	0.33	-0.01	-0.01	-0.03	-0.03
de01	~~	un12	0.33	-0.02	-0.02	-0.03	-0.03
ut11	~~	ut12	0.32	0.02	0.02	0.03	0.03
de05	~~	de08	0.32	-0.02	-0.02	-0.03	-0.03
co10	~~	de05	0.32	0.02	0.02	0.03	0.03
pr07	~~	ut04	0.32	-0.02	-0.02	-0.03	-0.03
pr02	~~	ut08	0.32	0.02	0.02	0.03	0.03
de02	~~	de06	0.31	0.02	0.02	0.03	0.03
pr08	~~	ut01	0.31	0.01	0.01	0.03	0.03
co02	~~	de06	0.31	-0.02	-0.02	-0.03	-0.03
ut06	~~	ut11	0.31	0.02	0.02	0.03	0.03
co09	~~	de03	0.31	0.02	0.02	0.03	0.03
pr09	~~	fa06	0.30	0.02	0.02	0.03	0.03
co05	~~	fa02	0.30	-0.02	-0.02	-0.03	-0.03
co08	~~	ut01	0.30	-0.02	-0.02	-0.03	-0.03
pr09	~~	un06	0.30	-0.02	-0.02	-0.03	-0.03
co04	~~	fa08	0.30	-0.03	-0.03	-0.03	-0.03
pr08	~~	ut08	0.30	0.01	0.01	0.03	0.03
de06	~~	un06	0.29	0.03	0.03	0.03	0.03
de01	~~	un03	0.29	-0.02	-0.02	-0.03	-0.03
co01	~~	ut06	0.29	-0.01	-0.01	-0.03	-0.03
co05	~~	ut09	0.29	-0.02	-0.02	-0.03	-0.03
de07	~~	un08	0.29	-0.02	-0.02	-0.03	-0.03
pr06	~~	ut02	0.29	-0.01	-0.01	-0.03	-0.03
co02	~~	ut11	0.28	-0.02	-0.02	-0.03	-0.03
pr01	~~	ut02	0.28	0.01	0.01	0.03	0.03
fa01	~~	un09	0.28	-0.02	-0.02	-0.03	-0.03
pr02	~~	ut11	0.28	0.02	0.02	0.02	0.02
co10	~~	fa05	0.28	0.02	0.02	0.03	0.03
ut08	~~	de09	0.28	-0.02	-0.02	-0.02	-0.02
pr08	~~	de07	0.28	0.01	0.01	0.03	0.03
co06	~~	ut05	0.28	0.02	0.02	0.03	0.03
pr02	~~	fa01	0.28	0.01	0.01	0.03	0.03
co02	~~	fa04	0.27	0.02	0.02	0.02	0.02
ut08	~~	ut09	0.27	0.02	0.02	0.03	0.03
fa06	~~	un05	0.27	-0.01	-0.01	-0.03	-0.03
pr09	~~	fa09	0.27	-0.02	-0.02	-0.02	-0.02
ut06	~~	fa01	0.26	0.01	0.01	0.03	0.03
pr06	~~	co03	0.26	0.02	0.02	0.02	0.02
pr08	~~	fa02	0.26	0.02	0.02	0.02	0.02
pr09	~~	co03	0.26	-0.02	-0.02	-0.02	-0.02
pr05	~~	ut03	0.26	-0.02	-0.02	-0.02	-0.02
pr09	~~	co06	0.26	-0.02	-0.02	-0.02	-0.02
de01	~~	un10	0.26	-0.02	-0.02	-0.02	-0.02
co01	~~	de08	0.26	0.01	0.01	0.03	0.03
ut06	~~	un02	0.26	0.01	0.01	0.03	0.03
pr06	~~	fa09	0.26	-0.02	-0.02	-0.02	-0.02
ut04	~~	fa02	0.26	0.02	0.02	0.02	0.02
fa08	~~	un12	0.26	0.02	0.02	0.02	0.02
pr02	~~	de07	0.26	-0.01	-0.01	-0.02	-0.02
de10	~~	un05	0.26	-0.01	-0.01	-0.03	-0.03
fa01	~~	un05	0.25	-0.01	-0.01	-0.03	-0.03
pr06	~~	fa10	0.25	-0.02	-0.02	-0.02	-0.02
un03	~~	un04	0.25	0.02	0.02	0.02	0.02
ut11	~~	un03	0.25	-0.02	-0.02	-0.02	-0.02
pr07	~~	fa05	0.25	0.01	0.01	0.03	0.03
ut08	~~	fa01	0.25	0.01	0.01	0.03	0.03
co05	~~	ut05	0.25	-0.02	-0.02	-0.03	-0.03
ut04	~~	ut12	0.25	-0.02	-0.02	-0.02	-0.02
pr09	~~	co02	0.24	0.01	0.01	0.02	0.02
co04	~~	co06	0.24	-0.02	-0.02	-0.02	-0.02
pr02	~~	fa08	0.24	0.02	0.02	0.02	0.02
ut07	~~	un05	0.24	-0.01	-0.01	-0.02	-0.02
co08	~~	un03	0.24	-0.02	-0.02	-0.02	-0.02
pr02	~~	co09	0.24	-0.01	-0.01	-0.02	-0.02
co10	~~	fa01	0.24	-0.01	-0.01	-0.03	-0.03
pr06	~~	un02	0.24	-0.01	-0.01	-0.02	-0.02
pr02	~~	de08	0.24	-0.01	-0.01	-0.02	-0.02
co05	~~	fa10	0.24	0.02	0.02	0.03	0.03
ut11	~~	de07	0.23	-0.02	-0.02	-0.02	-0.02
co10	~~	fa10	0.23	-0.02	-0.02	-0.02	-0.02
de01	~~	un01	0.23	-0.01	-0.01	-0.02	-0.02
co08	~~	ut06	0.22	0.02	0.02	0.02	0.02
ut01	~~	fa01	0.22	-0.01	-0.01	-0.03	-0.03
pr06	~~	pr08	0.22	0.01	0.01	0.02	0.02
co01	~~	co09	0.22	-0.01	-0.01	-0.03	-0.03
ut09	~~	de10	0.22	0.02	0.02	0.02	0.02
pr08	~~	co05	0.22	0.01	0.01	0.02	0.02
de09	~~	un01	0.22	0.02	0.02	0.02	0.02
pr10	~~	fa04	0.21	0.02	0.02	0.02	0.02
pr09	~~	ut03	0.21	0.02	0.02	0.02	0.02
fa02	~~	un07	0.21	-0.02	-0.02	-0.02	-0.02
pr07	~~	co05	0.21	0.01	0.01	0.02	0.02
ut02	~~	de08	0.21	0.01	0.01	0.02	0.02
ut05	~~	de06	0.21	-0.02	-0.02	-0.02	-0.02
ut11	~~	de03	0.21	0.02	0.02	0.02	0.02
pr02	~~	un03	0.21	-0.02	-0.02	-0.02	-0.02
pr07	~~	de09	0.21	-0.02	-0.02	-0.02	-0.02
un02	~~	un09	0.21	0.01	0.01	0.02	0.02
ut05	~~	ut06	0.21	-0.02	-0.02	-0.02	-0.02
pr05	~~	un04	0.21	0.02	0.02	0.02	0.02
de06	~~	un11	0.21	0.02	0.02	0.02	0.02
co03	~~	un03	0.21	0.02	0.02	0.02	0.02
pr10	~~	de06	0.20	-0.02	-0.02	-0.02	-0.02
co09	~~	ut02	0.20	0.01	0.01	0.02	0.02
fa02	~~	un02	0.20	0.02	0.02	0.02	0.02
co04	~~	ut06	0.20	0.02	0.02	0.02	0.02
ut03	~~	fa01	0.20	-0.01	-0.01	-0.02	-0.02
co08	~~	de06	0.20	-0.02	-0.02	-0.02	-0.02
ut04	~~	un10	0.20	-0.02	-0.02	-0.02	-0.02
ut11	~~	fa08	0.19	-0.02	-0.02	-0.02	-0.02
fa02	~~	un11	0.19	0.02	0.02	0.02	0.02
pr05	~~	fa01	0.19	0.01	0.01	0.02	0.02
pr07	~~	un05	0.18	-0.01	-0.01	-0.02	-0.02
fa01	~~	fa06	0.18	-0.01	-0.01	-0.03	-0.03
co08	~~	de01	0.18	0.02	0.02	0.02	0.02
pr09	~~	ut04	0.18	-0.02	-0.02	-0.02	-0.02
ut06	~~	un11	0.18	-0.01	-0.01	-0.02	-0.02
pr01	~~	un08	0.18	-0.01	-0.01	-0.02	-0.02
co02	~~	ut07	0.18	0.01	0.01	0.02	0.02
pr01	~~	un03	0.18	0.01	0.01	0.02	0.02
fa09	~~	de01	0.18	-0.02	-0.02	-0.02	-0.02
ut05	~~	un09	0.18	-0.02	-0.02	-0.02	-0.02
pr01	~~	pr07	0.18	-0.01	-0.01	-0.02	-0.02
pr08	~~	de01	0.17	-0.01	-0.01	-0.02	-0.02
ut07	~~	un02	0.17	-0.01	-0.01	-0.02	-0.02
ut12	~~	de02	0.17	0.01	0.01	0.02	0.02
de01	~~	un09	0.17	-0.02	-0.02	-0.02	-0.02
fa05	~~	fa08	0.17	-0.01	-0.01	-0.02	-0.02
ut01	~~	fa02	0.17	-0.01	-0.01	-0.02	-0.02
de07	~~	de10	0.17	-0.01	-0.01	-0.02	-0.02
fa02	~~	un05	0.17	-0.01	-0.01	-0.02	-0.02
co02	~~	fa08	0.17	0.02	0.02	0.02	0.02
fa05	~~	de08	0.17	0.01	0.01	0.02	0.02
ut01	~~	un02	0.17	-0.01	-0.01	-0.02	-0.02
pr05	~~	de07	0.17	-0.02	-0.02	-0.02	-0.02
pr01	~~	ut06	0.16	-0.01	-0.01	-0.02	-0.02
ut02	~~	ut05	0.16	-0.01	-0.01	-0.02	-0.02
fa01	~~	un02	0.16	0.01	0.01	0.02	0.02
pr10	~~	un05	0.16	0.01	0.01	0.02	0.02
pr05	~~	co08	0.16	0.02	0.02	0.02	0.02
de08	~~	un03	0.16	0.01	0.01	0.02	0.02
co06	~~	fa09	0.16	0.02	0.02	0.02	0.02
co06	~~	ut06	0.16	0.01	0.01	0.02	0.02
ut04	~~	ut11	0.16	0.02	0.02	0.02	0.02
co09	~~	ut07	0.16	0.01	0.01	0.02	0.02
pr08	~~	ut12	0.16	-0.01	-0.01	-0.02	-0.02
ut06	~~	un01	0.16	-0.01	-0.01	-0.02	-0.02
fa09	~~	de02	0.16	-0.02	-0.02	-0.02	-0.02
ut05	~~	un06	0.16	-0.02	-0.02	-0.02	-0.02
co06	~~	de05	0.16	0.02	0.02	0.02	0.02
pr06	~~	de01	0.15	0.01	0.01	0.02	0.02
pr02	~~	de10	0.15	-0.01	-0.01	-0.02	-0.02
ut09	~~	de05	0.15	-0.02	-0.02	-0.02	-0.02
pr08	~~	co02	0.15	0.01	0.01	0.02	0.02
fa08	~~	de03	0.15	-0.02	-0.02	-0.02	-0.02
pr05	~~	co02	0.15	0.01	0.01	0.02	0.02
fa01	~~	fa05	0.15	-0.01	-0.01	-0.04	-0.04
co08	~~	ut04	0.15	-0.02	-0.02	-0.02	-0.02
co10	~~	un07	0.15	-0.01	-0.01	-0.02	-0.02
de01	~~	un06	0.15	-0.02	-0.02	-0.02	-0.02
pr02	~~	ut05	0.15	-0.01	-0.01	-0.02	-0.02
de01	~~	de10	0.15	-0.01	-0.01	-0.02	-0.02
pr01	~~	de03	0.15	-0.01	-0.01	-0.02	-0.02
co05	~~	ut12	0.15	0.01	0.01	0.02	0.02
pr01	~~	ut08	0.15	0.01	0.01	0.02	0.02
pr06	~~	un06	0.15	-0.02	-0.02	-0.02	-0.02
ut11	~~	de09	0.14	-0.02	-0.02	-0.02	-0.02
pr06	~~	un07	0.14	0.01	0.01	0.02	0.02
co06	~~	co08	0.14	-0.02	-0.02	-0.02	-0.02
fa01	~~	de08	0.14	0.01	0.01	0.02	0.02
co08	~~	ut12	0.14	-0.01	-0.01	-0.02	-0.02
pr07	~~	fa06	0.14	-0.01	-0.01	-0.02	-0.02
fa01	~~	un03	0.14	-0.01	-0.01	-0.02	-0.02
ut12	~~	un08	0.14	0.01	0.01	0.02	0.02
ut04	~~	ut06	0.14	-0.01	-0.01	-0.02	-0.02
co09	~~	ut08	0.14	0.01	0.01	0.02	0.02
pr06	~~	ut09	0.14	-0.01	-0.01	-0.02	-0.02
ut07	~~	fa06	0.13	0.01	0.01	0.02	0.02
fa09	~~	de05	0.13	0.02	0.02	0.02	0.02
fa10	~~	de10	0.13	-0.01	-0.01	-0.02	-0.02
pr06	~~	pr07	0.13	0.01	0.01	0.02	0.02
fa08	~~	un01	0.13	0.01	0.01	0.02	0.02
ut03	~~	ut12	0.13	-0.01	-0.01	-0.02	-0.02
fa05	~~	un01	0.13	0.01	0.01	0.02	0.02
ut04	~~	un01	0.13	0.01	0.01	0.02	0.02
co10	~~	un05	0.13	-0.01	-0.01	-0.02	-0.02
pr05	~~	de08	0.13	-0.01	-0.01	-0.02	-0.02
co03	~~	fa02	0.13	-0.02	-0.02	-0.02	-0.02
pr02	~~	un06	0.12	0.01	0.01	0.02	0.02
co05	~~	fa08	0.12	-0.01	-0.01	-0.02	-0.02
pr02	~~	de05	0.12	0.01	0.01	0.02	0.02
pr09	~~	co01	0.12	0.01	0.01	0.02	0.02
co04	~~	fa06	0.12	-0.01	-0.01	-0.02	-0.02
pr01	~~	ut11	0.12	-0.01	-0.01	-0.02	-0.02
pr07	~~	un09	0.12	0.01	0.01	0.02	0.02
co05	~~	de06	0.12	-0.01	-0.01	-0.02	-0.02
co09	~~	fa06	0.12	-0.01	-0.01	-0.02	-0.02
pr09	~~	de03	0.12	0.01	0.01	0.02	0.02
pr01	~~	de08	0.12	-0.01	-0.01	-0.02	-0.02
co06	~~	ut11	0.12	0.01	0.01	0.02	0.02
de09	~~	un09	0.12	-0.02	-0.02	-0.02	-0.02
un05	~~	un12	0.12	0.01	0.01	0.02	0.02
pr10	~~	de02	0.12	0.01	0.01	0.02	0.02
co09	~~	fa05	0.11	-0.01	-0.01	-0.02	-0.02
pr08	~~	un10	0.11	-0.01	-0.01	-0.02	-0.02
ut09	~~	de02	0.11	0.01	0.01	0.02	0.02
ut08	~~	un04	0.11	-0.01	-0.01	-0.02	-0.02
co09	~~	fa08	0.11	0.01	0.01	0.02	0.02
co02	~~	ut12	0.11	0.01	0.01	0.02	0.02
de08	~~	un10	0.11	-0.01	-0.01	-0.02	-0.02
pr01	~~	pr10	0.11	0.01	0.01	0.02	0.02
co05	~~	fa06	0.11	0.01	0.01	0.02	0.02
co06	~~	de01	0.11	-0.01	-0.01	-0.02	-0.02
ut07	~~	un10	0.11	-0.01	-0.01	-0.02	-0.02
un02	~~	un05	0.11	-0.01	-0.01	-0.02	-0.02
ut08	~~	un05	0.10	0.01	0.01	0.02	0.02
fa05	~~	un09	0.10	0.01	0.01	0.02	0.02
fa09	~~	un05	0.10	-0.01	-0.01	-0.02	-0.02
fa08	~~	un05	0.10	0.01	0.01	0.02	0.02
fa10	~~	de01	0.10	0.01	0.01	0.02	0.02
ut12	~~	un02	0.10	-0.01	-0.01	-0.02	-0.02
ut03	~~	fa05	0.10	0.01	0.01	0.02	0.02
ut03	~~	un02	0.10	0.01	0.01	0.02	0.02
pr02	~~	un04	0.10	0.01	0.01	0.02	0.02
ut05	~~	fa05	0.10	0.01	0.01	0.02	0.02
ut02	~~	ut09	0.10	0.01	0.01	0.02	0.02
pr05	~~	co06	0.10	0.01	0.01	0.01	0.01
co10	~~	un04	0.10	-0.01	-0.01	-0.02	-0.02
pr02	~~	pr10	0.09	0.01	0.01	0.01	0.01
fa02	~~	de03	0.09	0.01	0.01	0.01	0.01
co06	~~	un04	0.09	0.01	0.01	0.01	0.01
pr01	~~	fa05	0.09	-0.01	-0.01	-0.02	-0.02
co02	~~	un08	0.09	-0.01	-0.01	-0.01	-0.01
co09	~~	ut11	0.09	0.01	0.01	0.01	0.01
fa06	~~	un04	0.09	0.01	0.01	0.01	0.01
pr07	~~	fa09	0.09	0.01	0.01	0.01	0.01
fa02	~~	de01	0.09	-0.01	-0.01	-0.01	-0.01
co08	~~	ut09	0.09	0.01	0.01	0.01	0.01
co10	~~	ut04	0.09	-0.01	-0.01	-0.01	-0.01
fa10	~~	de08	0.09	-0.01	-0.01	-0.01	-0.01
pr06	~~	pr09	0.09	-0.01	-0.01	-0.01	-0.01
co01	~~	co03	0.09	-0.01	-0.01	-0.01	-0.01
pr05	~~	ut09	0.08	0.01	0.01	0.01	0.01
pr01	~~	co01	0.08	0.01	0.01	0.02	0.02
co05	~~	fa05	0.08	0.01	0.01	0.02	0.02
co10	~~	de02	0.08	0.01	0.01	0.01	0.01
pr05	~~	de03	0.08	-0.01	-0.01	-0.01	-0.01
co09	~~	un02	0.08	-0.01	-0.01	-0.01	-0.01
ut02	~~	un08	0.08	0.01	0.01	0.01	0.01
co10	~~	de09	0.08	-0.01	-0.01	-0.01	-0.01
co02	~~	un03	0.08	-0.01	-0.01	-0.01	-0.01
ut07	~~	un12	0.08	0.01	0.01	0.01	0.01
ut12	~~	de08	0.08	0.01	0.01	0.01	0.01
co06	~~	ut04	0.08	-0.01	-0.01	-0.01	-0.01
pr02	~~	ut12	0.08	0.01	0.01	0.01	0.01
ut02	~~	fa05	0.08	-0.01	-0.01	-0.02	-0.02
ut04	~~	de05	0.08	-0.01	-0.01	-0.01	-0.01
ut01	~~	un08	0.07	0.01	0.01	0.01	0.01
pr06	~~	ut04	0.07	0.01	0.01	0.01	0.01
co03	~~	un09	0.07	-0.01	-0.01	-0.01	-0.01
co06	~~	de06	0.07	0.01	0.01	0.01	0.01
ut01	~~	un06	0.07	-0.01	-0.01	-0.01	-0.01
pr06	~~	ut07	0.07	0.01	0.01	0.01	0.01
pr10	~~	de07	0.07	-0.01	-0.01	-0.01	-0.01
de03	~~	un08	0.07	0.01	0.01	0.01	0.01
fa06	~~	de03	0.07	-0.01	-0.01	-0.01	-0.01
pr01	~~	un05	0.07	0.01	0.01	0.01	0.01
co05	~~	de03	0.07	-0.01	-0.01	-0.01	-0.01
ut06	~~	fa10	0.07	-0.01	-0.01	-0.01	-0.01
ut04	~~	un07	0.06	0.01	0.01	0.01	0.01
co03	~~	un11	0.06	-0.01	-0.01	-0.01	-0.01
fa06	~~	de01	0.06	0.01	0.01	0.01	0.01
ut05	~~	un03	0.06	-0.01	-0.01	-0.01	-0.01
ut04	~~	un03	0.06	-0.01	-0.01	-0.01	-0.01
ut11	~~	fa01	0.06	0.01	0.01	0.01	0.01
pr07	~~	co06	0.06	-0.01	-0.01	-0.01	-0.01
un09	~~	un12	0.06	0.01	0.01	0.01	0.01
ut03	~~	fa10	0.06	0.01	0.01	0.01	0.01
co02	~~	un01	0.06	0.01	0.01	0.01	0.01
co06	~~	de08	0.06	0.01	0.01	0.01	0.01
co02	~~	ut08	0.06	0.01	0.01	0.01	0.01
co08	~~	ut11	0.06	-0.01	-0.01	-0.01	-0.01
co08	~~	un04	0.06	0.01	0.01	0.01	0.01
pr10	~~	ut05	0.06	-0.01	-0.01	-0.01	-0.01
co05	~~	ut08	0.06	-0.01	-0.01	-0.01	-0.01
co01	~~	un05	0.05	-0.01	-0.01	-0.01	-0.01
pr10	~~	un06	0.05	-0.01	-0.01	-0.01	-0.01
pr10	~~	un01	0.05	0.01	0.01	0.01	0.01
co02	~~	fa10	0.05	0.01	0.01	0.01	0.01
co08	~~	un02	0.05	-0.01	-0.01	-0.01	-0.01
fa02	~~	fa05	0.05	-0.01	-0.01	-0.01	-0.01
co06	~~	un12	0.05	0.01	0.01	0.01	0.01
pr06	~~	ut12	0.05	-0.01	-0.01	-0.01	-0.01
pr07	~~	fa01	0.05	-0.01	-0.01	-0.01	-0.01
co01	~~	co08	0.05	0.01	0.01	0.01	0.01
de08	~~	un02	0.05	-0.01	-0.01	-0.01	-0.01
ut11	~~	de02	0.05	0.01	0.01	0.01	0.01
pr02	~~	fa06	0.05	-0.01	-0.01	-0.01	-0.01
co09	~~	un03	0.04	-0.01	-0.01	-0.01	-0.01
pr08	~~	ut09	0.04	-0.01	-0.01	-0.01	-0.01
pr06	~~	fa06	0.04	-0.01	-0.01	-0.01	-0.01
ut11	~~	un02	0.04	-0.01	-0.01	-0.01	-0.01
pr06	~~	ut05	0.04	-0.01	-0.01	-0.01	-0.01
pr09	~~	pr10	0.04	-0.01	-0.01	-0.01	-0.01
ut06	~~	fa06	0.04	-0.01	-0.01	-0.01	-0.01
pr02	~~	de02	0.04	0.01	0.01	0.01	0.01
ut12	~~	de09	0.04	0.01	0.01	0.01	0.01
pr08	~~	ut06	0.04	0.00	0.00	0.01	0.01
ut12	~~	un06	0.04	0.01	0.01	0.01	0.01
pr07	~~	un07	0.04	0.01	0.01	0.01	0.01
un02	~~	un10	0.04	0.01	0.01	0.01	0.01
ut12	~~	de05	0.04	-0.01	-0.01	-0.01	-0.01
ut02	~~	fa08	0.04	0.01	0.01	0.01	0.01
co03	~~	ut05	0.04	-0.01	-0.01	-0.01	-0.01
ut06	~~	de09	0.04	0.01	0.01	0.01	0.01
pr02	~~	ut01	0.04	0.00	0.00	-0.01	-0.01
pr02	~~	fa09	0.04	-0.01	-0.01	-0.01	-0.01
ut09	~~	fa09	0.03	-0.01	-0.01	-0.01	-0.01
pr01	~~	ut07	0.03	-0.01	-0.01	-0.01	-0.01
fa06	~~	de06	0.03	0.01	0.01	0.01	0.01
fa02	~~	un12	0.03	-0.01	-0.01	-0.01	-0.01
co02	~~	fa02	0.03	0.01	0.01	0.01	0.01
co09	~~	fa09	0.03	-0.01	-0.01	-0.01	-0.01
co02	~~	un05	0.03	0.00	0.00	0.01	0.01
co01	~~	un07	0.03	0.01	0.01	0.01	0.01
pr01	~~	ut01	0.03	0.00	0.00	-0.01	-0.01
co10	~~	de01	0.03	0.01	0.01	0.01	0.01
pr09	~~	un07	0.03	0.01	0.01	0.01	0.01
pr02	~~	un11	0.03	0.01	0.01	0.01	0.01
ut09	~~	fa10	0.03	0.01	0.01	0.01	0.01
co01	~~	ut02	0.03	0.00	0.00	-0.01	-0.01
ut06	~~	de06	0.03	0.01	0.01	0.01	0.01
ut08	~~	ut12	0.03	0.01	0.01	0.01	0.01
de06	~~	un10	0.03	-0.01	-0.01	-0.01	-0.01
co02	~~	un11	0.03	0.00	0.00	-0.01	-0.01
ut05	~~	ut09	0.03	0.01	0.01	0.01	0.01
ut05	~~	de02	0.03	0.01	0.01	0.01	0.01
co10	~~	ut05	0.03	0.01	0.01	0.01	0.01
ut03	~~	ut04	0.03	-0.01	-0.01	-0.01	-0.01
ut06	~~	un04	0.03	0.00	0.00	-0.01	-0.01
pr06	~~	co08	0.03	0.01	0.01	0.01	0.01
pr10	~~	co09	0.03	-0.01	-0.01	-0.01	-0.01
co10	~~	un09	0.03	-0.01	-0.01	-0.01	-0.01
pr07	~~	un06	0.03	-0.01	-0.01	-0.01	-0.01
fa01	~~	de09	0.02	0.01	0.01	0.01	0.01
pr07	~~	co01	0.02	0.00	0.00	0.01	0.01
pr05	~~	co05	0.02	0.01	0.01	0.01	0.01
co10	~~	un03	0.02	0.01	0.01	0.01	0.01
pr10	~~	ut08	0.02	-0.01	-0.01	-0.01	-0.01
co05	~~	ut03	0.02	-0.01	-0.01	-0.01	-0.01
ut02	~~	fa10	0.02	0.00	0.00	-0.01	-0.01
ut09	~~	un12	0.02	0.00	0.00	-0.01	-0.01
pr02	~~	co04	0.02	0.01	0.01	0.01	0.01
ut03	~~	fa06	0.02	0.01	0.01	0.01	0.01
pr06	~~	co01	0.02	0.00	0.00	0.01	0.01
de03	~~	un06	0.02	0.01	0.01	0.01	0.01
co09	~~	ut09	0.02	0.00	0.00	-0.01	-0.01
ut05	~~	fa02	0.02	-0.01	-0.01	-0.01	-0.01
co01	~~	fa01	0.02	0.00	0.00	-0.01	-0.01
pr10	~~	co08	0.02	0.01	0.01	0.01	0.01
fa05	~~	de06	0.02	0.00	0.00	0.01	0.01
pr05	~~	ut04	0.02	-0.01	-0.01	-0.01	-0.01
pr06	~~	un10	0.02	0.00	0.00	-0.01	-0.01
fa04	~~	un06	0.02	0.01	0.01	0.01	0.01
pr02	~~	un05	0.02	0.00	0.00	-0.01	-0.01
ut01	~~	de03	0.02	0.00	0.00	-0.01	-0.01
pr07	~~	co10	0.02	0.00	0.00	-0.01	-0.01
ut06	~~	de03	0.02	0.00	0.00	-0.01	-0.01
pr06	~~	pr10	0.02	0.00	0.00	-0.01	-0.01
ut02	~~	de06	0.02	0.00	0.00	0.01	0.01
de05	~~	un04	0.02	0.00	0.00	0.01	0.01
de06	~~	un08	0.02	0.00	0.00	-0.01	-0.01
fa08	~~	un10	0.02	0.01	0.01	0.01	0.01
ut02	~~	un04	0.02	0.00	0.00	-0.01	-0.01
ut04	~~	ut09	0.02	-0.01	-0.01	-0.01	-0.01
pr05	~~	co10	0.02	-0.01	-0.01	-0.01	-0.01
fa06	~~	de08	0.02	0.00	0.00	0.01	0.01
ut09	~~	un07	0.02	0.00	0.00	0.01	0.01
pr10	~~	fa09	0.02	-0.01	-0.01	-0.01	-0.01
ut09	~~	de01	0.01	0.00	0.00	0.01	0.01
co02	~~	de01	0.01	0.00	0.00	-0.01	-0.01
ut12	~~	fa04	0.01	0.00	0.00	0.01	0.01
co02	~~	de05	0.01	0.00	0.00	-0.01	-0.01
ut11	~~	fa09	0.01	0.01	0.01	0.01	0.01
fa06	~~	de09	0.01	0.01	0.01	0.01	0.01
ut07	~~	un11	0.01	0.00	0.00	-0.01	-0.01
ut02	~~	un02	0.01	0.00	0.00	0.01	0.01
ut05	~~	ut07	0.01	0.00	0.00	0.00	0.00
co01	~~	co04	0.01	0.00	0.00	0.00	0.00
ut07	~~	fa04	0.01	0.00	0.00	0.00	0.00
pr07	~~	ut11	0.01	0.00	0.00	0.00	0.00
pr07	~~	de07	0.01	0.00	0.00	0.00	0.00
co02	~~	un04	0.01	0.00	0.00	0.00	0.00
fa01	~~	un04	0.01	0.00	0.00	0.01	0.01
ut03	~~	de07	0.01	0.00	0.00	0.00	0.00
ut12	~~	un12	0.01	0.00	0.00	0.00	0.00
co01	~~	de05	0.01	0.00	0.00	0.00	0.00
co10	~~	un06	0.01	0.00	0.00	0.00	0.00
ut07	~~	un01	0.01	0.00	0.00	0.00	0.00
ut12	~~	fa05	0.01	0.00	0.00	0.00	0.00
pr05	~~	ut02	0.01	0.00	0.00	0.00	0.00
pr07	~~	un08	0.01	0.00	0.00	0.00	0.00
co06	~~	un06	0.01	0.00	0.00	0.00	0.00
co10	~~	ut09	0.01	0.00	0.00	0.00	0.00
pr10	~~	ut12	0.01	0.00	0.00	0.00	0.00
de08	~~	de09	0.01	0.00	0.00	0.00	0.00
de08	~~	un05	0.01	0.00	0.00	0.00	0.00
pr07	~~	ut05	0.01	0.00	0.00	0.00	0.00
un04	~~	un09	0.01	0.00	0.00	0.00	0.00
co09	~~	fa10	0.01	0.00	0.00	0.00	0.00
pr10	~~	de09	0.00	0.00	0.00	0.00	0.00
de07	~~	un10	0.00	0.00	0.00	0.00	0.00
un06	~~	un11	0.00	0.00	0.00	0.00	0.00
ut04	~~	fa09	0.00	0.00	0.00	0.00	0.00
ut04	~~	de06	0.00	0.00	0.00	0.00	0.00
co02	~~	co03	0.00	0.00	0.00	0.00	0.00
ut06	~~	ut12	0.00	0.00	0.00	0.00	0.00
ut04	~~	fa10	0.00	0.00	0.00	0.00	0.00
pr06	~~	co09	0.00	0.00	0.00	0.00	0.00
de10	~~	un06	0.00	0.00	0.00	0.00	0.00
pr02	~~	ut06	0.00	0.00	0.00	0.00	0.00
un06	~~	un09	0.00	0.00	0.00	0.00	0.00
fa06	~~	un11	0.00	0.00	0.00	0.00	0.00
ut03	~~	un08	0.00	0.00	0.00	0.00	0.00
pr07	~~	ut03	0.00	0.00	0.00	0.00	0.00
pr10	~~	un07	0.00	0.00	0.00	0.00	0.00
pr08	~~	de08	0.00	0.00	0.00	0.00	0.00
pr01	~~	fa09	0.00	0.00	0.00	0.00	0.00
fa08	~~	de02	0.00	0.00	0.00	0.00	0.00
pr02	~~	co03	0.00	0.00	0.00	0.00	0.00
fa01	~~	de06	0.00	0.00	0.00	0.00	0.00
fa01	~~	un07	0.00	0.00	0.00	0.00	0.00
fa06	~~	de05	0.00	0.00	0.00	0.00	0.00
fa04	~~	de08	0.00	0.00	0.00	0.00	0.00
co01	~~	de02	0.00	0.00	0.00	0.00	0.00
fa05	~~	un12	0.00	0.00	0.00	0.00	0.00
co01	~~	fa08	0.00	0.00	0.00	0.00	0.00
ut08	~~	un06	0.00	0.00	0.00	0.00	0.00
pr06	~~	un11	0.00	0.00	0.00	0.00	0.00
fa08	~~	de10	0.00	0.00	0.00	0.00	0.00
ut05	~~	un08	0.00	0.00	0.00	0.00	0.00
pr07	~~	pr10	0.00	0.00	0.00	0.00	0.00
co06	~~	un07	0.00	0.00	0.00	0.00	0.00
co02	~~	un06	0.00	0.00	0.00	0.00	0.00
co02	~~	de07	0.00	0.00	0.00	0.00	0.00
co03	~~	ut01	0.00	0.00	0.00	0.00	0.00
ut02	~~	un05	0.00	0.00	0.00	0.00	0.00
co06	~~	de03	0.00	0.00	0.00	0.00	0.00
co01	~~	co10	0.00	0.00	0.00	0.00	0.00
co05	~~	de08	0.00	0.00	0.00	0.00	0.00
ut08	~~	de05	0.00	0.00	0.00	0.00	0.00
co02	~~	fa09	0.00	0.00	0.00	0.00	0.00
co02	~~	de09	0.00	0.00	0.00	0.00	0.00
co10	~~	ut11	0.00	0.00	0.00	0.00	0.00
ut05	~~	fa04	0.00	0.00	0.00	0.00	0.00
pr02	~~	co08	0.00	0.00	0.00	0.00	0.00
de02	~~	un03	0.00	0.00	0.00	0.00	0.00
co06	~~	de02	0.00	0.00	0.00	0.00	0.00
ut06	~~	fa09	0.00	0.00	0.00	0.00	0.00
co06	~~	ut07	0.00	0.00	0.00	0.00	0.00
ut04	~~	fa01	0.00	0.00	0.00	0.00	0.00
co06	~~	fa05	0.00	0.00	0.00	0.00	0.00
co10	~~	un11	0.00	0.00	0.00	0.00	0.00
pr10	~~	ut11	0.00	0.00	0.00	0.00	0.00
de01	~~	de07	0.00	0.00	0.00	0.00	0.00

Factors correlations:

modif3 %>% 
  filter(op == "~~" & 
           lhs %in% c("PR", "CO", "UT", "FA", "DE", "UN") &
           rhs %in% c("PR", "CO", "UT", "FA", "DE", "UN")) %>% 
  kable(digits = 2,
        col.names = c("Factor", "", "Factor", "Modification Index", "epc", "sepc.lv", "sepc.all", "sepc.nox"))

Factor		Factor	Modification Index	epc	sepc.lv	sepc.all	sepc.nox
FA	~~	UN	18.86	-0.12	-0.24	-0.24	-0.24
PR	~~	UT	16.05	0.07	0.62	0.62	0.62
CO	~~	UT	13.65	-0.07	-0.24	-0.24	-0.24
FA	~~	DE	12.35	0.06	0.38	0.38	0.38
PR	~~	DE	9.73	-0.05	-1.22	-1.22	-1.22
PR	~~	FA	7.60	-0.06	-0.36	-0.36	-0.36
CO	~~	FA	2.64	0.04	0.10	0.10	0.10
DE	~~	UN	1.88	0.02	0.13	0.13	0.13
UT	~~	DE	1.58	-0.02	-0.16	-0.16	-0.16
PR	~~	CO	1.02	0.02	0.15	0.15	0.15
CO	~~	DE	0.67	0.01	0.10	0.10	0.10
PR	~~	UN	0.55	0.01	0.08	0.08	0.08
UT	~~	FA	0.23	-0.01	-0.03	-0.03	-0.03
UT	~~	UN	0.00	0.00	0.00	0.00	0.00
CO	~~	UN	0.00	0.00	0.00	0.00	0.00

Modificated model

mdl4 <- "
PR =~ pr01 + pr02 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa04 + fa05 + fa06 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
DT =~ PR + CO + UT + FA + DE + UN
de05 ~~ de09
pr05 ~~ de06
fa02 ~~ fa08
pr05 ~~ ut11
fa02 ~~ fa09
fa08 ~~ fa09
ut11 ~~ de06
"

model4 <- cfa(mdl4, taia %>% select(all_of(taia_items_2)))

summary(model4)

## lavaan 0.6-8 ended normally after 48 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       127
##                                                       
##   Number of observations                           495
##                                                       
## Model Test User Model:
##                                                       
##   Test statistic                              4321.703
##   Degrees of freedom                              1526
##   P-value (Chi-square)                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   PR =~                                               
##     pr01              1.000                           
##     pr02              0.728    0.056   13.027    0.000
##     pr05              0.717    0.063   11.369    0.000
##     pr06              0.811    0.062   13.157    0.000
##     pr07              1.054    0.062   17.060    0.000
##     pr08              0.859    0.050   17.015    0.000
##     pr09              0.731    0.055   13.317    0.000
##     pr10              0.668    0.061   11.018    0.000
##   CO =~                                               
##     co01              1.000                           
##     co02              0.892    0.061   14.528    0.000
##     co03              0.651    0.061   10.716    0.000
##     co04              0.475    0.065    7.307    0.000
##     co05              1.085    0.065   16.579    0.000
##     co06              0.859    0.066   13.092    0.000
##     co08              0.435    0.062    6.992    0.000
##     co09              0.973    0.063   15.443    0.000
##     co10              0.833    0.065   12.835    0.000
##   UT =~                                               
##     ut01              1.000                           
##     ut02              1.075    0.054   19.729    0.000
##     ut03              0.668    0.062   10.843    0.000
##     ut04              0.634    0.062   10.237    0.000
##     ut05              0.967    0.065   14.801    0.000
##     ut06              1.006    0.058   17.389    0.000
##     ut07              0.796    0.062   12.825    0.000
##     ut08              0.808    0.057   14.153    0.000
##     ut09              0.946    0.063   14.994    0.000
##     ut11              0.748    0.063   11.840    0.000
##     ut12              0.978    0.061   15.911    0.000
##   FA =~                                               
##     fa01              1.000                           
##     fa02              0.416    0.061    6.865    0.000
##     fa04              0.431    0.055    7.804    0.000
##     fa05              1.017    0.051   20.123    0.000
##     fa06              0.736    0.052   14.240    0.000
##     fa08              0.374    0.058    6.391    0.000
##     fa09              0.522    0.060    8.737    0.000
##     fa10              0.755    0.058   12.950    0.000
##   DE =~                                               
##     de01              1.000                           
##     de02              1.271    0.106   12.039    0.000
##     de03              1.149    0.105   10.967    0.000
##     de05              0.939    0.098    9.611    0.000
##     de06              1.001    0.096   10.446    0.000
##     de07              0.958    0.088   10.934    0.000
##     de08              1.146    0.096   11.925    0.000
##     de09              0.498    0.093    5.370    0.000
##     de10              1.319    0.109   12.146    0.000
##   UN =~                                               
##     un01              1.000                           
##     un02              1.212    0.064   18.941    0.000
##     un03              0.811    0.068   11.920    0.000
##     un04              1.022    0.062   16.533    0.000
##     un05              1.140    0.062   18.334    0.000
##     un06              0.779    0.072   10.820    0.000
##     un07              1.035    0.067   15.354    0.000
##     un08              1.118    0.066   16.979    0.000
##     un09              1.084    0.070   15.410    0.000
##     un10              1.046    0.066   15.909    0.000
##     un11              1.188    0.068   17.435    0.000
##     un12              1.061    0.064   16.565    0.000
##   DT =~                                               
##     PR                1.000                           
##     CO                0.769    0.062   12.436    0.000
##     UT                0.829    0.061   13.615    0.000
##     FA                0.836    0.066   12.631    0.000
##     DE                0.809    0.067   12.017    0.000
##     UN                0.417    0.054    7.689    0.000
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##  .de05 ~~                                             
##    .de09              0.701    0.062   11.257    0.000
##  .pr05 ~~                                             
##    .de06              0.601    0.054   11.139    0.000
##  .fa02 ~~                                             
##    .fa08              0.645    0.063   10.288    0.000
##  .pr05 ~~                                             
##    .ut11              0.581    0.056   10.365    0.000
##  .fa02 ~~                                             
##    .fa09              0.611    0.062    9.808    0.000
##  .fa08 ~~                                             
##    .fa09              0.562    0.060    9.417    0.000
##  .ut11 ~~                                             
##    .de06              0.518    0.054    9.624    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .pr01              0.373    0.029   12.957    0.000
##    .pr02              0.622    0.042   14.867    0.000
##    .pr05              1.022    0.067   15.227    0.000
##    .pr06              0.752    0.051   14.843    0.000
##    .pr07              0.570    0.042   13.729    0.000
##    .pr08              0.382    0.028   13.748    0.000
##    .pr09              0.590    0.040   14.813    0.000
##    .pr10              0.805    0.053   15.170    0.000
##    .co01              0.525    0.040   13.109    0.000
##    .co02              0.578    0.042   13.839    0.000
##    .co03              0.775    0.052   14.985    0.000
##    .co04              1.036    0.067   15.436    0.000
##    .co05              0.486    0.039   12.396    0.000
##    .co06              0.760    0.053   14.404    0.000
##    .co08              0.958    0.062   15.464    0.000
##    .co09              0.542    0.041   13.327    0.000
##    .co10              0.760    0.052   14.484    0.000
##    .ut01              0.440    0.033   13.477    0.000
##    .ut02              0.341    0.028   12.367    0.000
##    .ut03              0.929    0.061   15.258    0.000
##    .ut04              0.960    0.063   15.320    0.000
##    .ut05              0.840    0.057   14.633    0.000
##    .ut06              0.527    0.038   13.827    0.000
##    .ut07              0.858    0.057   15.003    0.000
##    .ut08              0.670    0.045   14.771    0.000
##    .ut09              0.773    0.053   14.588    0.000
##    .ut11              1.119    0.073   15.242    0.000
##    .ut12              0.683    0.048   14.347    0.000
##    .fa01              0.371    0.036   10.220    0.000
##    .fa02              1.245    0.080   15.465    0.000
##    .fa04              1.019    0.066   15.385    0.000
##    .fa05              0.343    0.036    9.613    0.000
##    .fa06              0.708    0.050   14.250    0.000
##    .fa08              1.171    0.076   15.502    0.000
##    .fa09              1.162    0.076   15.285    0.000
##    .fa10              0.958    0.066   14.588    0.000
##    .de01              0.794    0.054   14.778    0.000
##    .de02              0.676    0.049   13.906    0.000
##    .de03              0.889    0.061   14.605    0.000
##    .de05              0.992    0.066   15.059    0.000
##    .de06              0.964    0.064   14.947    0.000
##    .de07              0.626    0.043   14.620    0.000
##    .de08              0.581    0.041   14.007    0.000
##    .de09              1.334    0.086   15.587    0.000
##    .de10              0.690    0.050   13.803    0.000
##    .un01              0.497    0.035   14.366    0.000
##    .un02              0.399    0.030   13.228    0.000
##    .un03              0.966    0.063   15.272    0.000
##    .un04              0.541    0.038   14.423    0.000
##    .un05              0.423    0.031   13.641    0.000
##    .un06              1.149    0.075   15.375    0.000
##    .un07              0.731    0.050   14.739    0.000
##    .un08              0.581    0.041   14.271    0.000
##    .un09              0.792    0.054   14.726    0.000
##    .un10              0.657    0.045   14.603    0.000
##    .un11              0.584    0.041   14.092    0.000
##    .un12              0.578    0.040   14.412    0.000
##    .PR                0.059    0.017    3.421    0.001
##    .CO                0.314    0.038    8.159    0.000
##    .UT                0.291    0.033    8.734    0.000
##    .FA                0.460    0.049    9.428    0.000
##    .DE                0.051    0.014    3.767    0.000
##    .UN                0.514    0.055    9.332    0.000
##     DT                0.540    0.057    9.408    0.000

tibble(
  `Model 4` = c(
    "Chi-Squared",
    "DF",
    "p",
    "GFI",
    "AGFI",
    "CFI",
    "TLI",
    "SRMR",
    "RMSEA"
  ),
  Value = round(fitmeasures(
    model4,
    c(
      "chisq",
      "df",
      "pvalue",
      "gfi",
      "agfi",
      "cfi",
      "tli",
      "srmr",
      "rmsea"
    )
  ), 4)
) %>% kable()

Model 4	Value
Chi-Squared	4321.7034
DF	1526.0000
p	0.0000
GFI	0.7210
AGFI	0.6978
CFI	0.8085
TLI	0.7997
SRMR	0.0961
RMSEA	0.0608

Standardized solution:

smodel4 <- standardizedsolution(model4)

Loadings:

smodel4 %>%
  filter(op == "=~") %>%
  kable(
    col.names = c(
      "Factor",
      "",
      "Item",
      "Loading",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )

Factor		Item	Loading	SE	z	p	CI lower bound	CI upper bound
PR	=~	pr01	0.785	0.020	38.709	0	0.745	0.825
PR	=~	pr02	0.581	0.032	18.004	0	0.518	0.645
PR	=~	pr05	0.481	0.035	13.946	0	0.414	0.549
PR	=~	pr06	0.586	0.032	18.316	0	0.524	0.649
PR	=~	pr07	0.734	0.024	31.182	0	0.688	0.780
PR	=~	pr08	0.732	0.024	30.979	0	0.686	0.779
PR	=~	pr09	0.593	0.032	18.705	0	0.531	0.655
PR	=~	pr10	0.499	0.036	13.800	0	0.429	0.570
CO	=~	co01	0.740	0.024	30.666	0	0.692	0.787
CO	=~	co02	0.683	0.027	24.845	0	0.629	0.737
CO	=~	co03	0.508	0.037	13.897	0	0.436	0.579
CO	=~	co04	0.348	0.043	8.185	0	0.265	0.432
CO	=~	co05	0.778	0.022	35.752	0	0.735	0.821
CO	=~	co06	0.617	0.031	19.807	0	0.556	0.678
CO	=~	co08	0.333	0.043	7.754	0	0.249	0.418
CO	=~	co09	0.725	0.025	28.999	0	0.676	0.774
CO	=~	co10	0.605	0.032	19.048	0	0.543	0.668
UT	=~	ut01	0.775	0.020	37.826	0	0.735	0.815
UT	=~	ut02	0.832	0.017	49.845	0	0.799	0.864
UT	=~	ut03	0.491	0.036	13.553	0	0.420	0.562
UT	=~	ut04	0.466	0.037	12.469	0	0.392	0.539
UT	=~	ut05	0.651	0.028	23.122	0	0.596	0.706
UT	=~	ut06	0.748	0.022	33.641	0	0.705	0.792
UT	=~	ut07	0.573	0.032	17.696	0	0.510	0.637
UT	=~	ut08	0.626	0.030	21.160	0	0.568	0.684
UT	=~	ut09	0.659	0.028	23.749	0	0.604	0.713
UT	=~	ut11	0.499	0.034	14.863	0	0.433	0.564
UT	=~	ut12	0.694	0.026	27.020	0	0.643	0.744
FA	=~	fa01	0.832	0.019	42.754	0	0.794	0.871
FA	=~	fa02	0.323	0.044	7.419	0	0.237	0.408
FA	=~	fa04	0.364	0.042	8.629	0	0.281	0.447
FA	=~	fa05	0.847	0.019	45.113	0	0.810	0.883
FA	=~	fa06	0.625	0.031	20.189	0	0.565	0.686
FA	=~	fa08	0.301	0.044	6.834	0	0.215	0.388
FA	=~	fa09	0.405	0.041	9.915	0	0.325	0.485
FA	=~	fa10	0.577	0.033	17.223	0	0.511	0.642
DE	=~	de01	0.581	0.033	17.839	0	0.517	0.645
DE	=~	de02	0.701	0.026	27.084	0	0.650	0.752
DE	=~	de03	0.613	0.031	19.820	0	0.552	0.673
DE	=~	de05	0.515	0.036	14.381	0	0.444	0.585
DE	=~	de06	0.544	0.033	16.735	0	0.480	0.608
DE	=~	de07	0.610	0.031	19.650	0	0.549	0.671
DE	=~	de08	0.691	0.026	26.096	0	0.639	0.743
DE	=~	de09	0.265	0.045	5.945	0	0.177	0.352
DE	=~	de10	0.711	0.025	28.077	0	0.661	0.760
UN	=~	un01	0.742	0.022	33.778	0	0.699	0.785
UN	=~	un02	0.831	0.016	52.516	0	0.800	0.862
UN	=~	un03	0.541	0.033	16.194	0	0.476	0.607
UN	=~	un04	0.735	0.022	32.767	0	0.691	0.779
UN	=~	un05	0.807	0.018	46.042	0	0.773	0.842
UN	=~	un06	0.493	0.036	13.845	0	0.423	0.563
UN	=~	un07	0.686	0.025	26.957	0	0.637	0.736
UN	=~	un08	0.753	0.021	35.455	0	0.711	0.795
UN	=~	un09	0.689	0.025	27.199	0	0.639	0.738
UN	=~	un10	0.709	0.024	29.489	0	0.662	0.756
UN	=~	un11	0.771	0.020	38.552	0	0.732	0.810
UN	=~	un12	0.736	0.022	32.952	0	0.692	0.780
DT	=~	PR	0.949	0.015	64.540	0	0.921	0.978
DT	=~	CO	0.710	0.029	24.513	0	0.653	0.767
DT	=~	UT	0.749	0.025	29.589	0	0.699	0.798
DT	=~	FA	0.672	0.031	21.357	0	0.610	0.733
DT	=~	DE	0.934	0.016	59.214	0	0.903	0.965
DT	=~	UN	0.393	0.042	9.300	0	0.310	0.476

Covariances:

smodel4 %>%
  filter(op == "~~" & lhs != rhs) %>%
  kable(
    col.names = c(
      "Factor",
      "",
      "Factor",
      "Covariance",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )

Factor		Factor	Covariance	SE	z	p	CI lower bound	CI upper bound
de05	~~	de09	0.609	0.029	21.091	0	0.553	0.666
pr05	~~	de06	0.606	0.030	20.237	0	0.547	0.664
fa02	~~	fa08	0.534	0.032	16.465	0	0.471	0.598
pr05	~~	ut11	0.543	0.033	16.451	0	0.478	0.608
fa02	~~	fa09	0.508	0.034	15.001	0	0.442	0.575
fa08	~~	fa09	0.482	0.035	13.759	0	0.413	0.551
ut11	~~	de06	0.499	0.035	14.067	0	0.429	0.568

Residuals:

smodel4 %>%
  filter(op == "~~" & lhs == rhs) %>%
  select(-(2:3)) %>%
  kable(
    col.names = c(
      "Item",
      "Residual",
      "SE",
      "z",
      "p",
      "CI lower bound",
      "CI upper bound"
    ),
    digits = 3
  )

Item	Residual	SE	z	p	CI lower bound	CI upper bound
pr01	0.384	0.032	12.040	0	0.321	0.446
pr02	0.662	0.038	17.637	0	0.589	0.736
pr05	0.768	0.033	23.137	0	0.703	0.834
pr06	0.656	0.038	17.466	0	0.582	0.730
pr07	0.461	0.035	13.339	0	0.393	0.529
pr08	0.463	0.035	13.380	0	0.396	0.531
pr09	0.649	0.038	17.260	0	0.575	0.722
pr10	0.751	0.036	20.758	0	0.680	0.821
co01	0.453	0.036	12.702	0	0.383	0.523
co02	0.534	0.038	14.214	0	0.460	0.607
co03	0.742	0.037	20.020	0	0.670	0.815
co04	0.879	0.030	29.669	0	0.821	0.937
co05	0.395	0.034	11.658	0	0.328	0.461
co06	0.619	0.038	16.102	0	0.544	0.695
co08	0.889	0.029	31.020	0	0.833	0.945
co09	0.474	0.036	13.089	0	0.403	0.545
co10	0.634	0.038	16.468	0	0.558	0.709
ut01	0.399	0.032	12.580	0	0.337	0.462
ut02	0.309	0.028	11.121	0	0.254	0.363
ut03	0.759	0.036	21.293	0	0.689	0.828
ut04	0.783	0.035	22.515	0	0.715	0.851
ut05	0.576	0.037	15.700	0	0.504	0.648
ut06	0.440	0.033	13.230	0	0.375	0.505
ut07	0.672	0.037	18.087	0	0.599	0.744
ut08	0.608	0.037	16.418	0	0.536	0.681
ut09	0.566	0.037	15.496	0	0.495	0.638
ut11	0.751	0.033	22.463	0	0.686	0.817
ut12	0.519	0.036	14.579	0	0.449	0.589
fa01	0.307	0.032	9.478	0	0.244	0.371
fa02	0.896	0.028	31.898	0	0.841	0.951
fa04	0.867	0.031	28.217	0	0.807	0.928
fa05	0.283	0.032	8.919	0	0.221	0.346
fa06	0.609	0.039	15.727	0	0.533	0.685
fa08	0.909	0.027	34.200	0	0.857	0.961
fa09	0.836	0.033	25.281	0	0.771	0.901
fa10	0.668	0.039	17.290	0	0.592	0.743
de01	0.662	0.038	17.495	0	0.588	0.737
de02	0.508	0.036	13.997	0	0.437	0.579
de03	0.624	0.038	16.480	0	0.550	0.699
de05	0.735	0.037	19.964	0	0.663	0.807
de06	0.704	0.035	19.883	0	0.634	0.773
de07	0.628	0.038	16.559	0	0.553	0.702
de08	0.522	0.037	14.260	0	0.450	0.594
de09	0.930	0.024	39.497	0	0.884	0.976
de10	0.495	0.036	13.747	0	0.424	0.565
un01	0.450	0.033	13.798	0	0.386	0.514
un02	0.309	0.026	11.737	0	0.257	0.361
un03	0.707	0.036	19.564	0	0.636	0.778
un04	0.460	0.033	13.962	0	0.396	0.525
un05	0.348	0.028	12.310	0	0.293	0.404
un06	0.757	0.035	21.539	0	0.688	0.826
un07	0.529	0.035	15.126	0	0.460	0.597
un08	0.433	0.032	13.545	0	0.370	0.496
un09	0.526	0.035	15.068	0	0.457	0.594
un10	0.497	0.034	14.565	0	0.430	0.564
un11	0.405	0.031	13.128	0	0.345	0.466
un12	0.458	0.033	13.931	0	0.394	0.523
PR	0.098	0.028	3.526	0	0.044	0.153
CO	0.496	0.041	12.038	0	0.415	0.576
UT	0.439	0.038	11.589	0	0.365	0.514
FA	0.549	0.042	13.001	0	0.466	0.632
DE	0.127	0.029	4.312	0	0.069	0.185
UN	0.846	0.033	25.464	0	0.781	0.911
DT	1.000	0.000	NA	NA	1.000	1.000

semPaths(model4,
         what = "std",
         whatLabels = "est",
         style = "lisrel",
         residScale = 10,
         theme = "colorblind",
         rotation = 1,
         layout = "tree",
         cardinal = "lat cov",
         curvePivot = TRUE,
         sizeMan = 3,
         sizeLat = 7)

Validation

TAIA total score

taia %>% 
  select(id, all_of(taia_items_2)) %>% 
  pivot_longer(all_of(taia_items_2),
               names_to = "subscale",
               values_to = "score") %>% 
  mutate(subscale = str_remove_all(subscale, "[:digit:]{2}") %>% toupper()) %>% 
  group_by(id, subscale) %>% 
  summarise(total_score = sum(score)) %>% 
  ungroup() %>% 
  pivot_wider(id_cols = id,
              names_from = subscale,
              values_from = total_score) %>% 
  relocate(after = c(id, PR, CO, UT, FA, DE, UN)) %>% 
  mutate(DT = PR + CO + UT + FA + DE + UN) %>% 
  full_join(taia) -> taia

## `summarise()` regrouping output by 'id' (override with `.groups` argument)

## Joining, by = "id"

clrs <-
  c("darkred",
    "chocolate3",
    "goldenrod3",
    "darkgreen",
    "darkblue",
    "purple4")

Correlations with General Trust Scale

taia %>% 
  pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
               names_to = "subscale",
               values_to = "score") %>%
  mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) -> taia_l

taia_l %>% 
  ggplot(aes(score, gt_score, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale, scales = "free") +
  scale_color_manual(values = clrs) +
  guides(color = FALSE) +
  labs(x = "TAIA subscale total score",
       y = "General Trust Scale total score",
       title = "Corelations between General Trust and TAIA subscales") +
  theme(plot.title = element_text(hjust = .5))

## `geom_smooth()` using formula 'y ~ x'

Predictability

cor.test(taia$PR, taia$gt_score)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$gt_score
## t = 3.512, df = 493, p-value = 0.0004856
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.0690492 0.2410450
## sample estimates:
##       cor 
## 0.1562312

Consistency

cor.test(taia$CO, taia$gt_score)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$gt_score
## t = 3.842, df = 493, p-value = 0.000138
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.08362567 0.25480562
## sample estimates:
##       cor 
## 0.1705018

Utility

cor.test(taia$UT, taia$gt_score)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$gt_score
## t = 2.4679, df = 493, p-value = 0.01393
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.02255584 0.19668679
## sample estimates:
##      cor 
## 0.110469

Faith

cor.test(taia$FA, taia$gt_score)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$gt_score
## t = 1.6968, df = 493, p-value = 0.09036
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.01201538  0.16323459
## sample estimates:
##        cor 
## 0.07619805

Dependability

cor.test(taia$DE, taia$gt_score)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$gt_score
## t = 4.3405, df = 493, p-value = 1.726e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1055064 0.2753324
## sample estimates:
##       cor 
## 0.1918551

Understanding

cor.test(taia$UN, taia$gt_score)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$gt_score
## t = 3.5643, df = 493, p-value = 0.0004003
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.07136383 0.24323469
## sample estimates:
##       cor 
## 0.1584997

TAIA

taia %>% 
  ggplot(aes(DT, gt_score)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm", color = "black") +
  labs(x = "TAIA score", y = "General Trust Score",
       title = "Correlation between General Trust and TAIA") +
  theme(plot.title = element_text(hjust = .5))

## `geom_smooth()` using formula 'y ~ x'

cor.test(taia$DT, taia$gt_score)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$gt_score
## t = 4.6048, df = 493, p-value = 5.261e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1170296 0.2860807
## sample estimates:
##       cor 
## 0.2030679

Correlations with questions

taia_l %>% 
  ggplot(aes(score, n_dighelp, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale) +
  guides(color = FALSE) +
  scale_color_manual(values = clrs) +
  labs(x = "TAIA subscales total score",
       y = "Number of digital helpers",
       title = "Correlation TAIA subscales with number of digital helpers") +
  theme(plot.title = element_text(hjust = .5))

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 948 rows containing non-finite values (stat_smooth).

## Warning: Removed 948 rows containing missing values (geom_point).

cor.test(taia$PR, taia$n_dighelp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$n_dighelp
## t = 1.1715, df = 335, p-value = 0.2422
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.04325762  0.16955094
## sample estimates:
##        cor 
## 0.06387275

cor.test(taia$CO, taia$n_dighelp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$n_dighelp
## t = -0.96471, df = 335, p-value = 0.3354
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.15857803  0.05450713
## sample estimates:
##         cor 
## -0.05263456

cor.test(taia$UT, taia$n_dighelp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$n_dighelp
## t = 1.3367, df = 335, p-value = 0.1822
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.03426592  0.17828379
## sample estimates:
##       cor 
## 0.0728359

cor.test(taia$FA, taia$n_dighelp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$n_dighelp
## t = -0.70154, df = 335, p-value = 0.4835
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1445447  0.0688158
## sample estimates:
##         cor 
## -0.03830097

cor.test(taia$DE, taia$n_dighelp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$n_dighelp
## t = 1.1408, df = 335, p-value = 0.2548
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.04492669  0.16792625
## sample estimates:
##        cor 
## 0.06220708

cor.test(taia$UN, taia$n_dighelp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$n_dighelp
## t = 1.4962, df = 335, p-value = 0.1355
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.02558116  0.18668686
## sample estimates:
##       cor 
## 0.0814767

cor.test(taia$DT, taia$n_dighelp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$n_dighelp
## t = 1.0065, df = 335, p-value = 0.3149
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.05223317  0.16080034
## sample estimates:
##        cor 
## 0.05490843

taia_l %>%
  ggplot(aes(score, e_dighelp, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale) +
  guides(color = FALSE) +
  scale_color_manual(values = clrs) +
  labs(x = "TAIA subscales total score",
       y = "Estimate of dealing with digital helpers experience",
       title = "Correlation TAIA subscales with expirience of dealing with digital helpers") +
  theme(plot.title = element_text(hjust = .5))

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 948 rows containing non-finite values (stat_smooth).

## Warning: Removed 948 rows containing missing values (geom_point).

cor.test(taia$PR, taia$e_dighelp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$e_dighelp
## t = 6.4594, df = 335, p-value = 3.701e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2342924 0.4245384
## sample estimates:
##       cor 
## 0.3327975

cor.test(taia$CO, taia$e_dighelp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$e_dighelp
## t = 4.0996, df = 335, p-value = 5.199e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1144032 0.3179773
## sample estimates:
##      cor 
## 0.218567

cor.test(taia$UT, taia$e_dighelp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$e_dighelp
## t = 5.5088, df = 335, p-value = 7.21e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1871349 0.3832429
## sample estimates:
##      cor 
## 0.288208

cor.test(taia$FA, taia$e_dighelp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$e_dighelp
## t = 3.9708, df = 335, p-value = 8.772e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1076158 0.3117865
## sample estimates:
##       cor 
## 0.2120134

cor.test(taia$DE, taia$e_dighelp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$e_dighelp
## t = 5.5689, df = 335, p-value = 5.265e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1901671 0.3859219
## sample estimates:
##       cor 
## 0.2910883

cor.test(taia$UN, taia$e_dighelp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$e_dighelp
## t = 1.7091, df = 335, p-value = 0.08836
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.01400204  0.19784231
## sample estimates:
##        cor 
## 0.09297223

cor.test(taia$DT, taia$e_dighelp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$e_dighelp
## t = 6.3515, df = 335, p-value = 6.95e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2290252 0.4199649
## sample estimates:
##       cor 
## 0.3278389

taia_l %>% 
  ggplot(aes(score, n_socnet, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale) +
  guides(color = FALSE) +
  scale_color_manual(values = clrs) +
  labs(x = "TAIA subscales total score",
       y = "Number of social networks and social media",
       title = "Correlation TAIA subscales with number of social networks and social media") +
  theme(plot.title = element_text(hjust = .5))

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 360 rows containing non-finite values (stat_smooth).

## Warning: Removed 360 rows containing missing values (geom_point).

cor.test(taia$PR, taia$n_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$n_socnet
## t = 1.7563, df = 433, p-value = 0.07974
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.009995365  0.176726793
## sample estimates:
##        cor 
## 0.08410396

cor.test(taia$CO, taia$n_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$n_socnet
## t = -0.18106, df = 433, p-value = 0.8564
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.10263724  0.08538923
## sample estimates:
##          cor 
## -0.008700912

cor.test(taia$UT, taia$n_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$n_socnet
## t = 3.3825, df = 433, p-value = 0.0007836
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.06744242 0.25068403
## sample estimates:
##       cor 
## 0.1604453

cor.test(taia$FA, taia$n_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$n_socnet
## t = 2.2881, df = 433, p-value = 0.02261
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.01543692 0.20125076
## sample estimates:
##       cor 
## 0.1092986

cor.test(taia$DE, taia$n_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$n_socnet
## t = 3.6857, df = 433, p-value = 0.000257
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.08172823 0.26409798
## sample estimates:
##       cor 
## 0.1744083

cor.test(taia$UN, taia$n_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$n_socnet
## t = 2.8836, df = 433, p-value = 0.004128
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.04380848 0.22833688
## sample estimates:
##       cor 
## 0.1372634

cor.test(taia$DT, taia$n_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$n_socnet
## t = 3.3815, df = 433, p-value = 0.0007863
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.06739663 0.25064092
## sample estimates:
##       cor 
## 0.1604005

taia_l %>% 
  ggplot(aes(score, f_socnet, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale) +
  guides(color = FALSE) +
  scale_color_manual(values = clrs) +
  labs(x = "TAIA subscales total score",
       y = "Frequency of social networks and social media use",
       title = "Correlation TAIA subscales with frequency of social networks and social media use") +
  theme(plot.title = element_text(hjust = .5))

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 360 rows containing non-finite values (stat_smooth).

## Warning: Removed 360 rows containing missing values (geom_point).

cor.test(taia$PR, taia$f_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$f_socnet
## t = 1.1135, df = 433, p-value = 0.2661
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.04078842  0.14671995
## sample estimates:
##       cor 
## 0.0534368

cor.test(taia$CO, taia$f_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$f_socnet
## t = 0.83383, df = 433, p-value = 0.4048
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.05418487  0.13355691
## sample estimates:
##       cor 
## 0.0400394

cor.test(taia$UT, taia$f_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$f_socnet
## t = -0.18344, df = 433, p-value = 0.8545
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.10275052  0.08527558
## sample estimates:
##          cor 
## -0.008815392

cor.test(taia$FA, taia$f_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$f_socnet
## t = 0.096068, df = 433, p-value = 0.9235
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.08944245  0.09859416
## sample estimates:
##         cor 
## 0.004616666

cor.test(taia$DE, taia$f_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$f_socnet
## t = 0.53504, df = 433, p-value = 0.5929
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.06848192  0.11943552
## sample estimates:
##        cor 
## 0.02570387

cor.test(taia$UN, taia$f_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$f_socnet
## t = -0.29625, df = 433, p-value = 0.7672
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.10811106  0.07989175
## sample estimates:
##         cor 
## -0.01423547

cor.test(taia$DT, taia$f_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$f_socnet
## t = 0.36218, df = 433, p-value = 0.7174
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.07674342  0.11124072
## sample estimates:
##        cor 
## 0.01740244

taia_l %>% 
  ggplot(aes(score, e_socnet, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale) +
  guides(color = FALSE) +
  scale_color_manual(values = clrs) +
  labs(x = "TAIA subscales total score",
       y = "Estimate of dealing with recommender systems experience",
       title = "Correlation TAIA subscales with experience of dealing with recommender systems") +
  theme(plot.title = element_text(hjust = .5))

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 360 rows containing non-finite values (stat_smooth).

## Warning: Removed 360 rows containing missing values (geom_point).

cor.test(taia$PR, taia$e_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$e_socnet
## t = 4.6159, df = 433, p-value = 5.164e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1250906 0.3043865
## sample estimates:
##       cor 
## 0.2165639

cor.test(taia$CO, taia$e_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$e_socnet
## t = 5.3414, df = 433, p-value = 1.493e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1583093 0.3348224
## sample estimates:
##       cor 
## 0.2486289

cor.test(taia$UT, taia$e_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$e_socnet
## t = 4.0939, df = 433, p-value = 5.061e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1008503 0.2819433
## sample estimates:
##       cor 
## 0.1930402

cor.test(taia$FA, taia$e_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$e_socnet
## t = 3.2486, df = 433, p-value = 0.00125
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.06111661 0.24472172
## sample estimates:
##       cor 
## 0.1542505

cor.test(taia$DE, taia$e_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$e_socnet
## t = 5.5098, df = 433, p-value = 6.177e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1659355 0.3417581
## sample estimates:
##       cor 
## 0.2559625

cor.test(taia$UN, taia$e_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$e_socnet
## t = 1.7866, df = 433, p-value = 0.0747
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.008545219  0.178131408
## sample estimates:
##       cor 
## 0.0855438

cor.test(taia$DT, taia$e_socnet)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$e_socnet
## t = 5.4871, df = 433, p-value = 6.965e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1649116 0.3408280
## sample estimates:
##       cor 
## 0.2549784

Self driving cars and education AI

taia %>% 
  pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
               names_to = "subscale",
               values_to = "score") %>% 
  mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>% 
  ggplot(aes(score, selfdrexp, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale) +
  guides(color = FALSE) +
  scale_color_manual(values = clrs) +
  labs(x = "TAIA subscales total score",
       y = "Estimate of selfdriving car experience",
       title = "Correlation TAIA subscales with selfdriving car experience") +
  theme(plot.title = element_text(hjust = .5))

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 2892 rows containing non-finite values (stat_smooth).

## Warning: Removed 2892 rows containing missing values (geom_point).

cor.test(taia$PR, taia$selfdrexp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$selfdrexp
## t = 0.84968, df = 11, p-value = 0.4136
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3507759  0.7030276
## sample estimates:
##       cor 
## 0.2481747

cor.test(taia$CO, taia$selfdrexp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$selfdrexp
## t = -0.35361, df = 11, p-value = 0.7303
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6207428  0.4725725
## sample estimates:
##        cor 
## -0.1060178

cor.test(taia$UT, taia$selfdrexp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$selfdrexp
## t = 0.99003, df = 11, p-value = 0.3434
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3145201  0.7230638
## sample estimates:
##       cor 
## 0.2860336

cor.test(taia$FA, taia$selfdrexp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$selfdrexp
## t = -0.49537, df = 11, p-value = 0.6301
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6461169  0.4389957
## sample estimates:
##        cor 
## -0.1477201

cor.test(taia$DE, taia$selfdrexp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$selfdrexp
## t = -0.3201, df = 11, p-value = 0.7549
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6145242  0.4803441
## sample estimates:
##         cor 
## -0.09606636

cor.test(taia$UN, taia$selfdrexp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$selfdrexp
## t = -0.11887, df = 11, p-value = 0.9075
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5754466  0.5255391
## sample estimates:
##         cor 
## -0.03581772

cor.test(taia$DT, taia$selfdrexp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$selfdrexp
## t = 0.17179, df = 11, p-value = 0.8667
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5139042  0.5860113
## sample estimates:
##        cor 
## 0.05172814

taia %>% 
  pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
               names_to = "subscale",
               values_to = "score") %>% 
  mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>% 
  ggplot(aes(score, selfdrsafe, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale) +
  guides(color = FALSE) +
  scale_color_manual(values = clrs) +
  labs(x = "TAIA subscales total score",
       y = "Estimate of selfdriving car safety",
       title = "Correlation TAIA subscales with selfdriving car safety") +
  theme(plot.title = element_text(hjust = .5))

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 2892 rows containing non-finite values (stat_smooth).

## Warning: Removed 2892 rows containing missing values (geom_point).

cor.test(taia$PR, taia$selfdrsafe)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$selfdrsafe
## t = -0.13243, df = 11, p-value = 0.897
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5781736  0.5225747
## sample estimates:
##         cor 
## -0.03989866

cor.test(taia$CO, taia$selfdrsafe)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$selfdrsafe
## t = 0.31876, df = 11, p-value = 0.7559
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4806533  0.6142740
## sample estimates:
##        cor 
## 0.09566808

cor.test(taia$UT, taia$selfdrsafe)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$selfdrsafe
## t = -0.15671, df = 11, p-value = 0.8783
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5830211  0.5172388
## sample estimates:
##         cor 
## -0.04719733

cor.test(taia$FA, taia$selfdrsafe)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$selfdrsafe
## t = 0.98457, df = 11, p-value = 0.346
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3159418  0.7223097
## sample estimates:
##       cor 
## 0.2845836

cor.test(taia$DE, taia$selfdrsafe)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$selfdrsafe
## t = -0.91642, df = 11, p-value = 0.3791
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7127275  0.3336096
## sample estimates:
##        cor 
## -0.2663311

cor.test(taia$UN, taia$selfdrsafe)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$selfdrsafe
## t = -1.0936, df = 11, p-value = 0.2975
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7369795  0.2874227
## sample estimates:
##        cor 
## -0.3131557

cor.test(taia$DT, taia$selfdrsafe)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$selfdrsafe
## t = -0.34044, df = 11, p-value = 0.7399
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6183078  0.4756360
## sample estimates:
##        cor 
## -0.1021088

taia %>% 
  pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
               names_to = "subscale",
               values_to = "score") %>% 
  mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>% 
  ggplot(aes(score, eduaiexp, color = subscale)) +
  geom_point(alpha = .3) +
  geom_smooth(method = "lm") +
  facet_wrap(~ subscale) +
  guides(color = FALSE) +
  scale_color_manual(values = clrs) +
  labs(x = "TAIA subscales total score",
       y = "Estimate of dealing with education AI experience",
       title = "Correlation TAIA subscales with experience of dealing with education AI") +
  theme(plot.title = element_text(hjust = .5))

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 2298 rows containing non-finite values (stat_smooth).

## Warning: Removed 2298 rows containing missing values (geom_point).

cor.test(taia$PR, taia$eduaiexp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$PR and taia$eduaiexp
## t = 5.7393, df = 110, p-value = 8.512e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3232882 0.6111631
## sample estimates:
##      cor 
## 0.480047

cor.test(taia$CO, taia$eduaiexp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$CO and taia$eduaiexp
## t = 4.8673, df = 110, p-value = 3.807e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2553469 0.5625701
## sample estimates:
##       cor 
## 0.4209573

cor.test(taia$UT, taia$eduaiexp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UT and taia$eduaiexp
## t = 4.1902, df = 110, p-value = 5.648e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1991635 0.5207166
## sample estimates:
##       cor 
## 0.3710084

cor.test(taia$FA, taia$eduaiexp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$FA and taia$eduaiexp
## t = 3.4196, df = 110, p-value = 0.0008808
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1320205 0.4685858
## sample estimates:
##       cor 
## 0.3099826

cor.test(taia$DE, taia$eduaiexp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DE and taia$eduaiexp
## t = 6.8158, df = 110, p-value = 5.253e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3997687 0.6633847
## sample estimates:
##       cor 
## 0.5449038

cor.test(taia$UN, taia$eduaiexp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$UN and taia$eduaiexp
## t = 3.3073, df = 110, p-value = 0.001273
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1219943 0.4605948
## sample estimates:
##       cor 
## 0.3007422

cor.test(taia$DT, taia$eduaiexp)

## 
##  Pearson's product-moment correlation
## 
## data:  taia$DT and taia$eduaiexp
## t = 6.6377, df = 110, p-value = 1.249e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3876975 0.6553096
## sample estimates:
##       cor 
## 0.5347814

Demographic data analysis

Number of participants

nrow(taia)

## [1] 495

Gender

table(taia$sex)

## 
##   f   m 
## 233 262

round(table(taia$sex) / length(taia$sex), 2)

## 
##    f    m 
## 0.47 0.53

prop.test(x = c(233, 0.54*495), n = c(495, 495))

## 
##  2-sample test for equality of proportions with continuity correction
## 
## data:  c(233, 0.54 * 495) out of c(495, 495)
## X-squared = 4.4809, df = 1, p-value = 0.03428
## alternative hypothesis: two.sided
## 95 percent confidence interval:
##  -0.133451569 -0.005134289
## sample estimates:
##    prop 1    prop 2 
## 0.4707071 0.5400000

prop.test(x = c(262, 0.46*495), n = c(495, 495))

## 
##  2-sample test for equality of proportions with continuity correction
## 
## data:  c(262, 0.46 * 495) out of c(495, 495)
## X-squared = 4.4809, df = 1, p-value = 0.03428
## alternative hypothesis: two.sided
## 95 percent confidence interval:
##  0.005134289 0.133451569
## sample estimates:
##    prop 1    prop 2 
## 0.5292929 0.4600000

Age

taia %>% 
  ggplot(aes(age)) +
  geom_histogram(binwidth = 1, fill = "black") +
  facet_grid(sex ~ .,
             labeller = labeller(sex = c(f = "Females", m = "Males"))) +
  scale_x_continuous(breaks = seq(20, 80, 5)) + 
  labs(x = "Age", y = "Count",
       title = "Age distributions by gender")

taia %>% 
  group_by(sex) %>% 
  summarise(min = min(age),
            max = max(age),
            median = median(age),
            mean = mean(age))

## `summarise()` ungrouping output (override with `.groups` argument)

## # A tibble: 2 x 5
##   sex     min   max median  mean
##   <chr> <dbl> <dbl>  <dbl> <dbl>
## 1 f        17    81   34    35.5
## 2 m        18    61   35.5  36.1

t.test(taia$age ~ taia$sex)

## 
##  Welch Two Sample t-test
## 
## data:  taia$age by taia$sex
## t = -0.69515, df = 472.19, p-value = 0.4873
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.545920  1.215316
## sample estimates:
## mean in group f mean in group m 
##         35.4721         36.1374

Cities

taia %>% 
  mutate(city = tolower(city)) %>% 
  mutate(city = recode(city,
         "м" = "москва",
         "мск" = "москва",
         "saint-petersburg" = "санкт-петербург",
         "санкт петербург" = "санкт-петербург",
         "рф, санкт-петербург" = "санкт-петербург",
         "спб" = "санкт-петербург")) %>% 
  group_by(city) %>% 
  summarise(n = n()) %>% 
  arrange(desc(n)) %>% 
  kable(col.names = c("City", "Num. of subjects"))

## `summarise()` ungrouping output (override with `.groups` argument)

City	Num. of subjects
москва	81
санкт-петербург	28
екатеринбург	12
краснодар	12
казань	11
ростов-на-дону	11
самара	11
новосибирск	10
саратов	10
уфа	10
пермь	9
воронеж	8
пенза	7
челябинск	7
иркутск	5
калининград	5
красноярск	5
нижний новгород	5
саранск	5
ярославль	5
иваново	4
кемерово	4
кострома	4
омск	4
смоленск	4
томск	4
чебоксары	4
барнаул	3
брянск	3
владимир	3
волгоград	3
ижевск	3
йошкар-ола	3
киров	3
оренбург	3
петушки	3
таганрог	3
тамбов	3
тольятти	3
энгельс	3
архангельск	2
астрахань	2
белгород	2
бийск	2
братск	2
великий новгород	2
владивосток	2
ишим	2
красногорск	2
кропоткин	2
кузнецк	2
курск	2
магнитогорск	2
майкоп	2
мурманск	2
подольск	2
псков	2
рыбинск	2
рязань	2
сафоново	2
севастополь	2
стерлитамак	2
тула	2
тюмень	2
череповец	2
чита	2
шахты	2
-	1
1987	1
абакан	1
азов	1
алтай	1
анапа	1
анжеро-судженск	1
армавир	1
афанасьево	1
балашиха	1
белово	1
белореченск	1
березники	1
благовещенск	1
бронницы	1
бугульма	1
волгодонск	1
вологда	1
воркута	1
воткинск	1
всеволожск, ленинградская область	1
выборг	1
вязьма	1
г. миасс	1
геленджик	1
гусев	1
далматово	1
дербент	1
джанкой	1
дзержинск	1
дивногорск	1
добрянка	1
донецк	1
донецк рф	1
дубна	1
железногорск курская область	1
жигулевск	1
жуковский	1
зверево	1
зеленоград	1
златоуст	1
калтан	1
катайск	1
качуг	1
керчь	1
кизляр	1
кизнер	1
кинешма	1
киржач	1
кисловодск	1
кола	1
кондопога	1
королёв	1
красноусольский	1
красноуфимск	1
кривой рог	1
курган	1
луга	1
луховицы	1
мариинск	1
махачкала	1
минеральные воды	1
минск	1
мичуринск	1
набережные челны	1
нефтегорск	1
нижнекамск	1
новоалтайск.	1
новокузнецк	1
новокуйбышевск	1
новочебоксарск	1
няндома	1
орел	1
орехово-зуево	1
орловская область	1
пензенская область	1
первоуральск	1
пермский край, г.верещагино	1
покровское	1
полысаево	1
починок	1
прокопьевск	1
ревда	1
россия. ростовская область. город ростов-на-дону.	1
ростов	1
ростов на дону	1
ростов- на - дону	1
ряань	1
североморск	1
село тюбук	1
сибай	1
сіктівкар	1
славгород	1
сланцы	1
соликамск	1
сочи	1
ставрополь	1
старый оскол	1
суздаль	1
сургут	1
таганрог и макеевка	1
тайшет	1
татарстан	1
тверь	1
тобольск	1
ужур	1
ульяновск	1
уссурийск	1
усть-катав	1
хабаровск	1
чайковский	1
чапаевск	1
чкаловск	1
щёлково	1
электрогорск	1
электросталь	1
юрга	1

Education and Job

taia %>% 
  mutate(spec1 = tolower(spec1)) %>% 
  group_by(spec1) %>% 
  summarise(n = n()) %>% 
  arrange(desc(n)) %>% 
  kable()

## `summarise()` ungrouping output (override with `.groups` argument)

spec1	n
экономист	26
инженер	23
юриспруденция	14
юрист	14
бухгалтер	12
менеджмент	10
повар	9
психология	9
менеджер	6
экономика	6
нет	5
педагог	5
товаровед	5
менеджмент организации	4
программист	4
психолог	4
техник	4
технолог	4
швея	4
ветеринарный врач	3
геолог	3
инженер-конструктор	3
история	3
медсестра	3
механик	3
нет специальности	3
туризм	3
филолог	3
финансы и кредит	3
автомеханик	2
асу	2
биолог	2
бухгалтерия	2
водитель	2
геодезия	2
инженер механик	2
инженер-строитель	2
информатик-экономист	2
информатика и вычислительная техника	2
коррекционное образование	2
маркетолог	2
медик	2
оператор	2
оператор пк	2
перевод и переводоведение	2
программирование	2
радиотехник	2
сварщик	2
слесарь	2
строительство	2
технолог общественного питания	2
техносферная безопасность	2
товаровед-эксперт	2
торговля	2
учитель русского языка и литературы	2
филология	2
химия	2
эколог	2
экология	2
экономическое	2
электрик	2
электроэнергетика	2
-	1
(в школе) направление дизайна	1
9 классов, сейчас заканчиваю учебу в колледже	1
pr	1
radio	1
schienze del turismo	1
авто механик	1
автомеханник	1
автомобилестроение	1
авторихтовщик	1
агросервис транспорта и энергетики	1
актёр театра драмы и кино	1
артист	1
архитектор	1
архитектор-реставратор	1
бакалавр журналистики	1
безопасность жизнедеятельности	1
бизнес-информатика	1
биологические науки	1
биология	1
бух.учет	1
бухгалтер-экономист	1
бухгалтерский учет	1
бухгалтерский учет и аудит	1
бухучет	1
верстальщик	1
военный	1
вокалист	1
гму	1
горный инженер	1
гос служба	1
гостиничное дело	1
гостиничный сервис	1
государственное и муниципальное управление	1
гримёр	1
гум	1
дизайн	1
документоведение и архивоведение	1
документоведение и информационная аналитика	1
журналист	1
журналистика	1
зооинженер	1
иллюстратор	1
инженер автомобилестроения	1
инженер асои	1
инженер водоснабжения	1
инженер горный	1
инженер информационных технологий и систем	1
инженер конструктор	1
инженер конструктор технолог эва	1
инженер микроэлектроники	1
инженер по земельному кадастру	1
инженер связи	1
инженер строитель	1
инженер технолог	1
инженер-механик	1
инженер-экономист лесной промышленности и лесного хозяйства	1
инженер-электрик	1
информатик- экономист	1
информатика и икт	1
информационная безопасность	1
информационные системы	1
информационные системы в экономике	1
информационные системы и технологии	1
информационные технологии и системы	1
историк-исследователь	1
каменщик	1
кибернетик	1
кибернетика	1
коммерсант	1
коммерция	1
компьютерная инженерия	1
компьютерные системы управления	1
кондитер	1
консервный мастер	1
конструктор-дизайнер	1
культурология	1
лаборант	1
лечебное дело	1
лингвист-переводчик	1
логопедия	1
маляр	1
маркетинг (только начал обучение	1
мастер отделочных работ	1
мастер сельхоз производства	1
математика	1
математический факультет нгпу (неоконченное)	1
материаловедение	1
материаловедение и технология материалов	1
машинист	1
машинист крана	1
машины и технологии литейного производства	1
медицинская сестра	1
менеджер по персоналу	1
менеджер по продажам	1
менеджмент спорта и туризма	1
металловедение и термическая обработка металлов	1
металлургия	1
монтаж, наладка и эксплуатация электрического оборудования гражданских и промышленных зданий	1
монтажник	1
монтёр аэс	1
не училась	1
никакой	1
обработка металлов давлением	1
общеобразовательная школп	1
омд	1
омс	1
оператор эвм	1
отсутствует	1
пед	1
педагог-электротехник	1
педагогическое	1
педиатрия	1
переводчик	1
повар кондитер	1
повар-технолог	1
повор	1
помощник машиниста	1
право	1
правовед	1
преподаватель	1
преподаватель английского языка	1
преподаватель начальных классов	1
преподаватель рус. яз., лит-ры и ин. языка	1
преподаватель экономики	1
прессовщик изделий из пластмасс с умением работать на литьевых машинах	1
прикладная геодезия	1
прикладная информатика в экономике	1
программист в компьютерных сетях	1
программное обеспечение вычислительных систем	1
продавец	1
проектирование радио	1
проектирование, эксплуатация, инжиниринг аэс	1
промешленное и гражданское строительство	1
промышленное и гражданское строительство	1
промышленный дизайн	1
психология бак + персонология маг	1
рабочий	1
радиомонтажник	1
радиотехника	1
редактор (газетно-журнальное издательское дело)	1
режиссёр	1
режиссура кино и телевидения	1
ремонт железнодорожного пути и путёвого хозяйство	1
русский язык и литература	1
рыбмастер	1
связь	1
секретарь	1
сервис и техническая эксплуатация транспортных и технологических машин.	1
сети связи	1
сис админ	1
системный администратор	1
системотехник	1
систему автоматического управления, но не защитил диплом	1
слесарь мср	1
слесарь по ремонту подвижного состава	1
соц.работа	1
социальная педагогика	1
социальная работа	1
социально-гуманитарное направление	1
социология	1
специалист по сервису	1
специалист по таможенному оформлению	1
специальности нет	1
среднее профессиональное образование	1
станочник широкого профиля	1
старший техник по и ас	1
стилист парикмахер	1
страховое дело	1
строитель	1
строитель кровельщик отделочник	1
строительство железных дорог	1
строительство и эксплуатация зданий и сооружений	1
тактико-специальная подготовка	1
таможенное дело	1
таможенное доле	1
телекоммуникации	1
техник - строитель	1
техник по информационным системам	1
техник программист	1
техник эвм	1
техник-коммерсант	1
техник-связи	1
техник-строитель	1
технолог молока и молочной продукции	1
технолог органических веществ	1
технолог поп	1
технология интернет	1
технология продуктов общественного питания	1
технология художественной обработки материалов	1
токарь	1
у меня нет специальности	1
управление персоналом	1
учет и аудит	1
училась на педагога по истории, но не окончила (незаконченное высшее образование - более трех лет)	1
учитель английского языка	1
учитель истории	1
учитель математики	1
учитель матнматики и физики	1
учитель начальных классов	1
учитель права	1
учитель технологии	1
учитель физической культуры и обж	1
учитель французского и немецкого языка	1
фармация	1
фельдшер	1
финансы и экономика	1
фрезеровщик	1
химик	1
художник	1
художник-мультипсикатор	1
школа	1
школьник	1
штукатур-маляр	1
экономика и бухгалтерский учёт	1
экономист-математик	1
экономическая безопасность	1
экспертиза и управление недвижимостью	1
электогазосварщик	1
электро монтёр связи	1
электрогазосварщик	1
электромеханик	1
электромонтёр	1
электроника и наноэлектроника	1
электронщик	1
электросварщик	1
электроснабжение	1
энергетик	1
юридическое	1
юрисконсульт	1
юриспруденөия	1

taia %>% 
  mutate(spec2 = tolower(spec2)) %>% 
  mutate(spec2 = recode(spec2, "-2" = "NA")) %>% 
  group_by(spec2) %>% 
  summarise(n = n()) %>% 
  arrange(desc(n)) %>% 
  kable()

## `summarise()` ungrouping output (override with `.groups` argument)

spec2	n
NA	428
бухгалтер	4
экономист	3
юрист	3
метролог	2
сварщик	2
design	1
автоматизация м связь	1
администратор салона красоты	1
архитектор	1
асу	1
библиотековед-библиограф	1
бухучет	1
водитель троллейбуса	1
гос управление	1
графический дизайн	1
дефектология	1
инженер виноделия	1
иностранный язык	1
культуролог	1
логопед	1
мастер отделочных и строительных работ	1
машинист крана	1
медбрат	1
медицинская сестра	1
менеджмент	1
менеджмент в сфере туризма	1
механик	1
наладчик холодильных установок	1
налогообложение	1
общий и стратегический менеджмент	1
оператор котельной	1
парикмахер-универсал	1
педагогика	1
переводчик	1
повар	1
правовед	1
преподаватель	1
программист	1
психология (неоконченное образование)	1
радиомонтажник	1
редактор	1
реставратор	1
сестринское дело	1
специалист	1
специалист аэропорта	1
специалист по охране труда	1
специалист по туристическим услугам	1
строительство и эксплуатация зданий и сооружений	1
телефонист	1
техник	1
тренер-преподаватель	1
управление персоналом	1
ученый-агроном	1
филология	1
финансовый менеджер	1
финансы	1
экономика	1
юриспруденция	1

Comparison trust level of digital specialisations with other

specs <- c(
  "программист",
  "информатик-экономист",
  "информатика и вычислительная техника",
  "оператор пк",
  "программирование",
  "бизнес-информатика",
  "информатик- экономист",
  "информатика и икт",
  "информационная безопасность",
  "информационные системы",
  "информационные системы в экономике",
  "информационные системы и технологии",
  "информационные технологии и системы",
  "кибернетика",
  "компьютерная инженерия",
  "компьютерные системы управления",
  "прикладная информатика в экономике",
  "программист в компьютерных сетях",
  "программное обеспечение вычислительных систем",
  "сис админ",
  "системный администратор",
  "техник по информационным системам",
  "техник программист"
)

taia %>% 
  mutate(spec1 = tolower(spec1),
         spec2 = tolower(spec2),
         dig_spec = ifelse(spec1 %in% specs | spec2 %in% specs, TRUE, FALSE)) -> taia

t.test(taia$DT ~ taia$dig_spec)

## 
##  Welch Two Sample t-test
## 
## data:  taia$DT by taia$dig_spec
## t = -2.6551, df = 34.335, p-value = 0.01194
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -25.820608  -3.435371
## sample estimates:
## mean in group FALSE  mean in group TRUE 
##            152.9849            167.6129

---

taia %>% 
  mutate(jobfield = tolower(jobfield)) %>% 
  group_by(jobfield) %>% 
  summarise(n = n()) %>% 
  arrange(desc(n)) %>% 
  kable()

## `summarise()` ungrouping output (override with `.groups` argument)

jobfield	n
-	156
it	18
строительство	16
продажи	14
образование	12
торговля	12
не работаю	9
производство	9
транспорт	8
логистика	7
медицина	6
фриланс	6
охрана	5
самозанятый	5
финансы	5
жкх	4
информационные технологии	4
машиностроение	4
энергетика	4
_	3
безопасность	3
графический дизайн	3
грузоперевозки	3
культура	3
металлургия	3
ржд	3
самозанятая	3
–	2
-1	2
hr	2
банк	2
бухгалтерия	2
здравоохранение	2
ит	2
обслуживание	2
общепит	2
пенсионер	2
продажа	2
проектирование	2
реклама	2
социальная	2
спорт	2
телекоммуникации	2
туризм	2
фрилансер	2
юридическая сфера	2
____	1
—	1
—–	1
—————-	1
—	1
3-печать	1
it (1c)	1
it-сфера	1
it-технологии	1
it, коммуникации	1
orm/smm	1
smm	1
автомобильный бизнес	1
адвокат	1
атб	1
банковское дело	1
безопасность персонала	1
бизнес, торговля	1
биология, медицина	1
бухгалтер	1
бухгалтерский учет	1
в интернете	1
визажист	1
геодезия	1
госструктура	1
гостинечный бизнес	1
гостиничный бизнес	1
государственная служба	1
дизайн	1
дизайн интерьеров	1
добыча полезных ископаемых	1
иллюстрация	1
интернет	1
информатика	1
информационная безопасность	1
информационные технологии в медицине	1
ип	1
ип в сфере туризма	1
искусство	1
искусство и культура	1
кинопроизводство	1
колл центр	1
коммунальные услуги	1
консалтинг	1
литература, театр.	1
логистика складская	1
маркетинг	1
мебель	1
медиа	1
музыка	1
музыкальное образование	1
музыкант	1
мфц	1
мчс россии	1
налоговая сфера	1
научно-исследовательская деятельность, ветеринария, эпидемиология	1
недвижимость	1
нефте добыча	1
нефтяная	1
нефтянка	1
обслуживание и ремонт контрольно-кассовой техники	1
обслуживание населения	1
оказание услуг	1
онлайн-обучение	1
оптовая торговля	1
остин	1
охрана труда	1
охранная деятельность	1
пенсионерка	1
пивной блогер	1
пномышленность	1
право	1
программист	1
продажа кухонной мебели, дизайн	1
продажа мебели	1
производство мебели	1
производство металлоконструкций	1
производство продуктов питания	1
производство систем охлаждения	1
производство х/булочных изделий	1
промышленное производство	1
рабочий на заводе	1
развлечения	1
разметка информации	1
ремонт бытовой техники	1
ремонт электроники	1
ресторанный бизнес	1
рыболовство	1
самозанятый it специалист	1
связь	1
связь и телекоммуникации	1
сельское хозяйство и производство	1
склад	1
служба доставки	1
соц защита	1
социалка	1
стоматология	1
страхование	1
строителство	1
строительство и дизайн	1
строительство, ремонт	1
сфера жкх	1
сфера обслуживания	1
сфера розничных продаж	1
сфера услуг	1
такси	1
телекоммуникация	1
телекоммуникация.	1
телемуникация	1
тестирование	1
техника	1
технологии	1
типография	1
транпортные перевозки	1
умные охранные системы	1
университет	1
управление персоналом	1
услуги населению, бюджетная организация.	1
фармация	1
флористика	1
частный предприниматель	1
экологическое проектирование	1
экономика	1
эконофика	1
экстренная служба	1
электронная подпись	1
юзабилити	1
юриспруденция	1
я работаю в сфере it	1

Comparison trust level of digital job fields with other

jobs <- c(
  "it",
  "информационные технологии",
  "ит",
  "it (1c)",
  "it-сфера",
  "it-технологии",
  "it, коммуникации",
  "информатика",
  "информационная безопасность",
  "информационные технологии в медицине",
  "программист",
  "самозанятый it специалист",
  "я работаю в сфере it",
  "юзабилити",
  "техника",
  "технологии"
)

taia %>% 
  mutate(jobfield = tolower(jobfield),
         dig_job = ifelse(jobfield %in% jobs, TRUE, FALSE)) -> taia

t.test(taia$DT ~ taia$dig_job)

## 
##  Welch Two Sample t-test
## 
## data:  taia$DT by taia$dig_job
## t = -0.62167, df = 40.728, p-value = 0.5376
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -15.225044   8.058987
## sample estimates:
## mean in group FALSE  mean in group TRUE 
##            153.6332            157.2162

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